main_tutorial_bias_word_embedding.ipynb

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      "source": [
        "# Diagnosing and mitigating bias in word embeddings\n",
        "\n",
        "Based on the tutorial https://learn.responsibly.ai/word-embedding\n",
        "and its [notebook](https://colab.research.google.com/github/ResponsiblyAI/word-embedding/blob/main/tutorial-bias-word-embedding.ipynb).\n",
        "\n",
        "Powered by [`responsibly`](https://docs.responsibly.ai/) - Toolkit for auditing and mitigating bias of machine learning systems.\n",
        "\n",
        "This is a shortened version of the original tutorial. For more extensive context, references and explanations, refer to https://learn.responsibly.ai/.\n",
        "\n",
        "\n",
        "## Disclaimer\n",
        "\n",
        "Aiming for simplicity in the explanation, the concept of gender is oversimplified, treated as binary. Nevertheless, gender is a complex social construct. This should kept in mind when we go back from a learning context to the real-world.\n",
        "\n",
        "\n",
        "## Overview\n",
        "\n",
        "In this 45 minute tutorial, we will explore bias in **word embeddings** - a widespread building block of many machine learning models that work with human languages. \n",
        "\n",
        "A word embedding is an n-dimensional space where words are represented as vectors. In such space, words with similar behavior in text are close to each other. Following the **distributional hypothesis** (Harris 1954), behavior in text is often used as a proxy for the meaning of words. \n",
        "\n",
        "The dimensions in this particular space have been found  by a neural network, processing huge amounts of raw text data, such as news, wikipedia articles, reddit posts, tweets, etc. However, the resulting space is not intepretable at human level, since the resulting dimensions display no meaning that can be understood using common knowledge or common sense.\n",
        "\n",
        "Nevertheles, word embeddings have an easy-to-explain geometrical representation that allows an intuitive understanding of this building block and its potential biases. \n",
        "\n",
        "Bias can affect any aspect of human judgement. To illustrate the phenomenon and how to address it in word embeddings, we focus on **gender** bias. We use an oversimplified representation of gender, considering only binary gender. This simplification is aimed to improve understanding of the techniques, and needs to be specifically worked out for actual deployment in applications. \n",
        "\n",
        "## What we will be doing in this tutorial: \n",
        "\n",
        "1. **Define:** We have decided that we wish to work on gender bias, but gender bias is a very complex phenomena. First of all, we will **narrow down** our definition to be able to apply the available techniques. \n",
        "We will be working specifically on gender bias with respect to *occupations*.  \n",
        "\n",
        "2. **Diagnose:** Determine whether our embedding model has observable bias according to our definition based on occupations.\n",
        "\n",
        "3. **Mitigate:** Once we've observed some bias in our embedding, we will apply some techniques to reduce the effect of bias in the embedding.\n",
        "\n",
        "In order to do that, we use the techniques developed by [Bolukbasi et all](https://arxiv.org/pdf/1607.06520.pdf) (2016). They consist in: \n",
        "\n",
        "1. **Identifying the gender sub-space within the embedding space**: we will be characterizing a direction, or in a more general sense a sub-space, in which we can distinguish the components of the gender bias. \n",
        "\n",
        "2. In order to mitigate the bias but also be able to mantain the desirable and useful characteristics of the embedding, there are 2 techniques that the authors presents and are available in ```responsibly```:\n",
        "\n",
        "  2.1. **Neutralize**: given a *set of words that should be unbiased*, in our case professions that should be gender neutral, this technique ensures that these words are in the middle of the previously identified subspace.\n",
        "\n",
        "  2.2 **Equalize**: this technique works on *words* that should be neutral regarding to sets of words that represent a concept in different gender directions, for example {grandmother, grandfather}. After the equalization, the words that we consider should be neutral regarding gender (or the particular bias) will be equidistant to all the words of the set.\n",
        "\n",
        "In this short version we will only be working on neutralization.\n",
        "\n",
        "In all these techniques we refer to *sets of words* that are relevant to our bias analysis. These sets of words are a **key component** in all of the techniques we will be working on this workshop, and should be carefully elaborated for each particular use case.\n",
        "\n",
        "We encourage you to work beyond the gender example and try to model forms of bias that are relevant for your community. Try to formalize them as binary concepts and find words that should be gender neutral!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ND0y48N0C3u1"
      },
      "source": [
        "There are many examples of the impact of bias\n",
        "in language technology.\n",
        "In [`this notebook`](https://colab.research.google.com/drive/1leU5YU_XYzq8Mcn5xqLivkRycorncxnW) we've collected a few."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "idhysORlC3uy"
      },
      "source": [
        " # Part 0: Setup"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vn-4xIh6C3uz"
      },
      "source": [
        "## 🛠️ Install `responsibly`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 676
        },
        "id": "_nQ508U3C3uz",
        "outputId": "bd923560-e33e-450e-bc6f-311057029830"
      },
      "source": [
        "%pip install --user responsibly"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting responsibly\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/5a/a5/1cbc6653d0fbdba238934112eeada26219e4aebec69632d15555c2546dd5/responsibly-0.1.3-py3-none-any.whl (28.2MB)\n",
            "\u001b[K     |████████████████████████████████| 28.2MB 139kB/s \n",
            "\u001b[?25hCollecting gensim<3.8,>=3.7\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/fa/82/2542fac981c1f9302164127088bb2d2044bf70b18ed181bc745b4432f51a/gensim-3.7.3-cp37-cp37m-manylinux1_x86_64.whl (24.2MB)\n",
            "\u001b[K     |████████████████████████████████| 24.2MB 2.5MB/s \n",
            "\u001b[?25hCollecting matplotlib<3,>=2.2\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/d3/38/64d5579936b9058c39c443f186e3ee20c0a4bb0ccc9d3618a1be93b01d05/matplotlib-2.2.5-cp37-cp37m-manylinux1_x86_64.whl (12.8MB)\n",
            "\u001b[K     |████████████████████████████████| 12.8MB 18.5MB/s \n",
            "\u001b[?25hRequirement already satisfied: seaborn>=0.9 in /usr/local/lib/python3.7/dist-packages (from responsibly) (0.11.1)\n",
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            "Requirement already satisfied: tqdm>=4.24 in /usr/local/lib/python3.7/dist-packages (from responsibly) (4.41.1)\n",
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            "Requirement already satisfied: numpy>=1.15 in /usr/local/lib/python3.7/dist-packages (from responsibly) (1.19.5)\n",
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            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib<3,>=2.2->responsibly) (1.3.1)\n",
            "Requirement already satisfied: pytz in /usr/local/lib/python3.7/dist-packages (from matplotlib<3,>=2.2->responsibly) (2018.9)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib<3,>=2.2->responsibly) (2.8.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib<3,>=2.2->responsibly) (0.10.0)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.19->responsibly) (1.0.1)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from mlxtend<0.17,>=0.13->responsibly) (57.0.0)\n",
            "\u001b[31mERROR: plotnine 0.6.0 has requirement matplotlib>=3.1.1, but you'll have matplotlib 2.2.5 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: mizani 0.6.0 has requirement matplotlib>=3.1.1, but you'll have matplotlib 2.2.5 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: arviz 0.11.2 has requirement matplotlib>=3.0, but you'll have matplotlib 2.2.5 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: albumentations 0.1.12 has requirement imgaug<0.2.7,>=0.2.5, but you'll have imgaug 0.2.9 which is incompatible.\u001b[0m\n",
            "Installing collected packages: gensim, matplotlib, responsibly\n",
            "Successfully installed gensim-3.7.3 matplotlib-2.2.5 responsibly-0.1.3\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "matplotlib",
                  "mpl_toolkits"
                ]
              }
            }
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7KVoylBYC3u0"
      },
      "source": [
        "\n",
        "**Restart** the Kernel/Runtime (use the menu on top or the botton in the notebook), **skip** the installation cell (`!pip install --user responsibly`) and **run** the cell below (`import responsibly`)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rU1uolhsC3u0"
      },
      "source": [
        "## 🛠️Validate the installation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "E63RXd26C3u0",
        "outputId": "b1c2174d-780c-4aab-bbc4-66d5400e6057"
      },
      "source": [
        "import responsibly\n",
        "\n",
        "# You should get '0.1.3'\n",
        "responsibly.__version__\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'0.1.3'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oNwtwpu6A3G4"
      },
      "source": [
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U7PbQUuTIFSS"
      },
      "source": [
        "# Part 1: Playing with Word Embeddings"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0pCZrVekC3u2"
      },
      "source": [
        "## 1.1 Introduction and references\n",
        "\n",
        "When it comes to language modeling, it is clear that words are not that easy to represent in a way that captures the complexity of human language. In the case of images, for example, we translate a picture into vectors which represents pixels and their intensity, color, brightness, etc. We transform an image into a mathematical object that computers can manipulate. So, how can we represent words in a machine-usable way?\n",
        "\n",
        "There are many ways to do so, but in this tutorial we will be focusing on word embeddings. A word embedding is a learned representation for text where words that have similar meanings tend to have similar representations. But what does it mean that words have similar meanings? How can we \"teach\" that to a computer?\n",
        "\n",
        "\n",
        "**Distributional Hypothesis**\n",
        "\n",
        "> \"a word is characterized by the company it keeps\" - [John Rupert Firth](https://en.wikipedia.org/wiki/John_Rupert_Firth)\n",
        "\n",
        "Based on this hypothesis, word embeddings aim to represent words characterizing them by their contexts of occurrence, roughly following these steps:\n",
        "\n",
        "1. Gather huge amounts of text, say, all Wikipedia articles; then\n",
        "2. break text down into all possible substrings, using a sliding window along text;\n",
        "3. consider each substring as a training example for a neural network. This network tries to learn how to predict the context given one of the words in the substring, or it tries to predict one of the words given the context, that is, the rest of the words in the substring;\n",
        "4. once a neural network is trained with such examples, take the last-but-one layer as the space where words will be now represented, so that each neuron in that layer is a dimension of the embedding space.\n",
        "\n",
        "If you are curious about the details, [this post gives a very understandable visual introduction and pointers to reference work](http://jalammar.github.io/illustrated-word2vec/). \n",
        "\n",
        "It is important to note that, since embeddings are obtained from text created by people, they will naturally reflect their biases and prejudices. They should not be viewed as objective or neutral, but rather as a social construct, with the complexities of our societies."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NiqGEbVsCUN6"
      },
      "source": [
        "## 1.2 Dataset and basic properties\n",
        "\n",
        "The library [`responsibly`](http://docs.responsibly.ai) has the function [`responsibly.we.load_w2v_small`]() which returns  [`KeyedVectors`](https://radimrehurek.com/gensim/models/keyedvectors.html#gensim.models.keyedvectors.KeyedVectors) de [`gensim`](https://radimrehurek.com/gensim/). These word embeddings were trained using Google News: 100B tokens, 3 millon vocabulary, 300 dimensions vectors.\n",
        "\n",
        "For more information: [Word2Vec](https://code.google.com/archive/p/word2vec/) - "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o2yChPuMHPSo"
      },
      "source": [
        "from responsibly.we import load_w2v_small\n",
        "\n",
        "w2v_small = load_w2v_small()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p3mwmab7Fh9v",
        "outputId": "e6ba5e9d-9944-4bc6-9cca-d2ed2a966c4d"
      },
      "source": [
        "# dataset vocabulary size\n",
        "\n",
        "len(w2v_small.vocab)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "26423"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bAkayy2RG--n",
        "outputId": "d955725f-16da-4858-b341-6409624117b6"
      },
      "source": [
        "# get the vector of the word \"love\"\n",
        "\n",
        "print('love =', w2v_small['love'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "love = [ 0.0386357  -0.0571296   0.00970471  0.0618904  -0.0618904   0.0250858\n",
            "  0.109865   -0.0988782  -0.0527351   0.0754404  -0.00984205 -0.0307621\n",
            " -0.0103914  -0.0164797  -0.088258    0.0637215   0.0483405   0.0589607\n",
            "  0.00283817 -0.0261844  -0.0144655   0.0298466   0.0860607  -0.0538337\n",
            "  0.0629891  -0.0131838   0.0206912   0.0401006   0.0419317  -0.061158\n",
            " -0.0419317   0.0523688   0.00583656  0.0479742   0.0578621   0.028931\n",
            "  0.0981458   0.0324101  -0.00943005  0.125246    0.0699472  -0.0787364\n",
            "  0.0265506   0.00975049 -0.0399175  -0.0384526   0.0461432   0.0176699\n",
            "  0.00828563  0.0218814  -0.0411993   0.056031   -0.0401006   0.00583656\n",
            "  0.0336918   0.0421148  -0.0163881  -0.0426641  -0.00140192 -0.00682077\n",
            "  0.0911877   0.0316777  -0.0265506   0.0677499   0.0131838  -0.0362553\n",
            " -0.0823985  -0.00123026 -0.0119936   0.0692148   0.106935   -0.032227\n",
            " -0.0419317   0.00801097 -0.114992   -0.0346074  -0.0710458   0.00567634\n",
            "  0.069581    0.128908   -0.116457    0.0845958   0.0327763  -0.084962\n",
            " -0.110597    0.0309452  -0.144289    0.0952161   0.101076    0.0236209\n",
            " -0.00359349  0.0886242  -0.0670175  -0.0466925  -0.0648202  -0.044129\n",
            "  0.0739756  -0.0130007  -0.039002   -0.0615242  -0.0739756   0.0743418\n",
            "  0.035706    0.00210574  0.0472418   0.00027609 -0.0151064  -0.01149\n",
            "  0.00608834 -0.0615242  -0.0831309   0.064454    0.0450445  -0.00737009\n",
            "  0.166994    0.0243533   0.0222476  -0.0615242  -0.00512702  0.0710458\n",
            "  0.0208743  -0.0187686  -0.00533302  0.0596932   0.0278324  -0.118654\n",
            " -0.0200503  -0.00883495 -0.0637215   0.0234378  -0.0527351  -0.0494391\n",
            " -0.0479742   0.0452276   0.0220645  -0.00020886  0.0216067  -0.0316777\n",
            "  0.0637215   0.0512702  -0.0351567   0.0302128  -0.0150148  -0.0140993\n",
            " -0.0988782   0.0024834  -0.00723276  0.0353398  -0.0494391  -0.104738\n",
            "  0.104005    0.119386    0.0377202   0.0428472  -0.104005    0.0413824\n",
            " -0.0261844  -0.032227   -0.0417486  -0.104738   -0.0291141   0.0191348\n",
            " -0.0231631   0.168459    0.00700387 -0.0563972   0.0512702  -0.0563972\n",
            " -0.084962    0.0446783   0.0225223  -0.0146486  -0.0406499   0.0108949\n",
            "  0.0087434   0.0494391   0.00375371  0.0118105  -0.0472418  -0.0512702\n",
            " -0.114992   -0.106935    0.0369878  -0.00211718 -0.0314945  -0.0600594\n",
            " -0.0541999  -0.0684823   0.0375371   0.0162966   0.0399175   0.0618904\n",
            "  0.0236209   0.0659188   0.0593269   0.0468756   0.064454   -0.0212405\n",
            " -0.0338749  -0.079835   -0.0146486  -0.0124513  -0.0699472  -0.0710458\n",
            " -0.00682077 -0.0114442  -0.066285    0.0571296   0.00732431  0.006363\n",
            " -0.0406499   0.0677499   0.00186541 -0.0435797  -0.0743418  -0.0717783\n",
            "  0.0802012   0.0336918  -0.061158   -0.00121881 -0.0267337   0.0309452\n",
            "  0.00810252  0.0379033  -0.109132   -0.0179446  -0.0126344  -0.0541999\n",
            " -0.0239871  -0.0466925  -0.0930188   0.0347905   0.0043717   0.0926526\n",
            "  0.143557    0.00910961 -0.0459601   0.00097276 -0.00585945  0.00202563\n",
            "  0.0177615  -0.00279239 -0.0684823   0.0823985  -0.0622567   0.134767\n",
            "  0.0198672  -0.0142824  -0.039002   -0.0692148   0.0280155   0.10254\n",
            "  0.106203    0.0571296   0.0192263  -0.0578621  -0.0589607  -0.0809337\n",
            " -0.0743418   0.037354   -0.0344243   0.0281986  -0.00915539  0.0651864\n",
            " -0.0422979   0.00082399  0.0280155  -0.00865184  0.0324101   0.0622567\n",
            "  0.0291141   0.0552986   0.0578621   0.0428472   0.0545661  -0.090089\n",
            " -0.0791026   0.0278324  -0.0552986  -0.0324101   0.0604256  -0.0267337\n",
            "  0.0410162   0.066285    0.069581   -0.0593269  -0.084962    0.0563972\n",
            " -0.0320439   0.0342412  -0.0119936   0.0505378  -0.0567634  -0.160402\n",
            " -0.0129091  -0.104738    0.0468756  -0.0743418  -0.0461432   0.0243533 ]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZkaSBw8-L4cX"
      },
      "source": [
        "There is not much to see for us humans. While really useful, are not easily interpretable. The encoding of the semantics of the word is not transparent to us users, there is not a direct relationship between the value of the vector coordinate and a particular word meaning. They encode lots of information in a sort of criptic way. So, is there a way to detect/diagnose/observe bias in word embeddings?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bhXogvjGHdit"
      },
      "source": [
        "## 1.3 Measuring distance between words\n",
        "\n",
        "### Measure of Similiarty: [Cosine Similariy](https://en.wikipedia.org/wiki/Cosine_similarity)\n",
        "- Measures the cosine of the angle between two vectors.\n",
        "- Ranges between 1 (same vector) to -1 (opposite vector)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d-7nbBcsHPSo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4c038aec-23d6-4697-a124-05946226ab10"
      },
      "source": [
        "w2v_small['cat'] @ w2v_small['cat']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0000006"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jgPVPnfsIj-d",
        "outputId": "23f41f8f-2147-4421-8e26-50c90a881c45"
      },
      "source": [
        "w2v_small['cat'] @ w2v_small['cats']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8099383"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yBUuXExdImLN",
        "outputId": "c7d2cc47-5e8b-4651-a49d-59ab01a8304d"
      },
      "source": [
        "w2v_small['cat'] @ w2v_small['dog']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7609462"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m18k2bP1IpmY",
        "outputId": "b53e6431-56fe-4fe1-e0da-8c7bc3adef75"
      },
      "source": [
        "w2v_small['cat'] @ w2v_small['horse']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.38147917"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TZ3Os5n3IusO",
        "outputId": "30ba7122-06af-49a0-f21e-a7a7be3be3be"
      },
      "source": [
        "w2v_small['cat'] @ w2v_small['finance']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-0.037760373"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hOqF6_g9MKbk",
        "outputId": "b1fcf49b-f89b-444b-ea53-3f8fdd771fb3"
      },
      "source": [
        "w2v_small.most_similar('cat', topn=5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('cats', 0.8099379539489746),\n",
              " ('dog', 0.7609457969665527),\n",
              " ('kitten', 0.7464984655380249),\n",
              " ('feline', 0.7326232194900513),\n",
              " ('puppy', 0.707545280456543)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RGM-q7TLRm7N"
      },
      "source": [
        "###Let's find \"the odd one out\""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "5zgV7HPmPJbQ",
        "outputId": "a558cd03-ec46-49f2-efaf-c50323bf7580"
      },
      "source": [
        "w2v_small.doesnt_match('hairdresser nurse man woman housekeeper personal_trainer'.split())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'nurse'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "lJHjGaBjWSb9",
        "outputId": "04ea5a47-c134-485c-c2e7-cdc15c8204b2"
      },
      "source": [
        "w2v_small.doesnt_match('hairdresser nurse man woman housekeeper manager'.split())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'manager'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "WVEqp38MRwg4",
        "outputId": "1b36e364-af96-4180-eae8-09874b0ad5ce"
      },
      "source": [
        "w2v_small.doesnt_match('hairdresser nurse man men housekeeper manager'.split())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'manager'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "F2A2TpWwYLmI",
        "outputId": "97b47625-7b76-4e55-9ccc-f239af302dd6"
      },
      "source": [
        "w2v_small.doesnt_match('hairdresser nurse woman women housekeeper manager'.split())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'manager'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s1vX3TKdv-6P"
      },
      "source": [
        "## 1.4 Visualization of Word Embedding using T-SNE\n",
        "\n",
        "Below you can find a visualization of word embeddings, a projection of a 300-dimensional embedding to a two-dimensional space. We can see that words with similar meanings tend to be close, like \"*yesterday*\", \"*summer*\" or \"*morning*\", \"*ever*\" and \"*never*\", \"*started*\" and \"*began*\"..."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 668
        },
        "id": "TDhPnNz5Lnfb",
        "outputId": "14513e0a-4c5a-4d9c-fc26-fdae2f2a5606"
      },
      "source": [
        "from sklearn.manifold import TSNE\n",
        "from matplotlib import pylab as plt\n",
        "\n",
        "# take the most common words in the corpus between 200 and 600\n",
        "words = [word for word in w2v_small.index2word[200:600]]\n",
        "\n",
        "# convert the words to vectors\n",
        "embeddings = [w2v_small[word] for word in words]\n",
        "\n",
        "# perform T-SNE\n",
        "words_embedded = TSNE(n_components=2).fit_transform(embeddings)\n",
        "\n",
        "# ... and visualize!\n",
        "plt.figure(figsize=(20, 20))\n",
        "for i, label in enumerate(words):\n",
        "    x, y = words_embedded[i, :]\n",
        "    plt.scatter(x, y)\n",
        "    plt.annotate(label, xy=(x, y), xytext=(5, 2), textcoords='offset points',\n",
        "                 ha='right', va='bottom', size=11)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x1440 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L4jB1Ex7FeJ5"
      },
      "source": [
        "To get a grasp on some basic embeddings properties, check [this notebook](https://colab.research.google.com/drive/1oCTFCSSkB08_ASWDkpsFYzjW3PvbI60b?usp=sharing)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O9Q-3tQdC3u_"
      },
      "source": [
        "## 1.5 Vector analogies\n",
        "\n",
        "One interesting property about word embeddings is that we can easily generate analogies. How do we do that? For example, if we want to obtain an analogy of the form \"*man is to king as woman is to ?*\", we substract \"*man*\" from \"*king + woman*\". \n",
        "Intuitively, we are factoring out the \"*man*\" part of the combination between a king and a woman."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vTl21bZiC3u_"
      },
      "source": [
        "<img src=\"https://github.com/ResponsiblyAI/word-embedding/blob/main/images/linear-relationships.png?raw=1\" />\n",
        "\n",
        "<small>Source: [Tensorflow Documentation](https://www.tensorflow.org/tutorials/representation/word2vec)</small>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z3b91gWTzvYJ"
      },
      "source": [
        "\n",
        "Keep in mind the word embedding was generated by learning the co-occurrence of words, so the fact that it *empirically* exhibit \"concept arithmetic\", it doesn't necessarily mean it learned meaning! In fact, it seems it didn't, but rather, it seems it learns how words behave but not the meaning they convey.\n",
        "See: [king - man + woman is queen; but why? by Piotr Migdał](https://p.migdal.pl/2017/01/06/king-man-woman-queen-why.html)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i9rqEENs0B-M"
      },
      "source": [
        "With these vectors, we can perform the kind of arithmetic operations described above:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9gwM2qSOl7Jp",
        "outputId": "9486f216-d2d6-43fe-a3a8-da1c425c2ed4"
      },
      "source": [
        "# small is to smaller as good is to what?\n",
        "w2v_small.most_similar(positive=['good', 'smaller'], negative=['small'], topn=2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('better', 0.7270160913467407), ('stronger', 0.5597214102745056)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N4cVRxWRUhmX",
        "outputId": "40f294ae-5a9a-4a2c-9895-4ec3c92ef7f5"
      },
      "source": [
        "# man is to carpenter as woman is to what?\n",
        "\n",
        "w2v_small.most_similar(positive=['carpenter', 'woman'], negative=['men'], topn=2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('schoolteacher', 0.590914785861969), ('homemaker', 0.5572819709777832)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RbiFSeRmPo0R"
      },
      "source": [
        "With these analogies we can start **exploring** our embedding to detect possible harmful bias."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jvOi3PHfNmoy"
      },
      "source": [
        "# Part 2: Diagnosing Gender Bias\n",
        "\n",
        "Given that the representation of a word in an embedding space is a vector, how can we **observe** bias? Can vectors be biased? What does that even mean?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VFoKGcr2C3vA"
      },
      "source": [
        "## 2.1 Words with mophological gender\n",
        "\n",
        "There are many words that are gender marked in their form, for example, \"*brother-sister*\". These words convey gender through their morphology. They do not necessarily reflect a stereotype, but a morphological characteristic of the words. Other words that convey gender are queen-king, mother-father, waiter-waitress."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mzHt7krQC3vB",
        "outputId": "748ab8c6-690f-4fd8-93ec-8d51e2d4ff42"
      },
      "source": [
        "# She is to sister as he is to what?\n",
        "# sister - she + he = ?\n",
        "\n",
        "w2v_small.most_similar(positive=['sister', 'he'], negative=['she'], topn=5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('brother', 0.7627110481262207),\n",
              " ('younger_brother', 0.6856132745742798),\n",
              " ('cousin', 0.6685015559196472),\n",
              " ('uncle', 0.6580697894096375),\n",
              " ('nephew', 0.65260249376297)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JmdyMQbBC3vB"
      },
      "source": [
        "## 2.2 Words with gender stereotypes \n",
        "\n",
        "In contrast, some words do not have a gender meaning morphologically, but are associated gender meanings by sterotyped usage. This happens because the contexts of occurrence of such words are biased toward some specific gender, reflecting how people actually use those words. Some words representing professions are markedly biased with respect to gender, because they tend to be associated to male or female roles. Here we here we explore such words the analogy mechanism."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1Lq26hcCC3vB",
        "outputId": "c5469bc0-4885-4f88-f138-fabfb61bfc61"
      },
      "source": [
        "#She is to interior designer as he is to what?\n",
        "w2v_small.most_similar(positive=['interior_designer', 'he'], negative=['she'], topn=4)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('architect', 0.623257040977478),\n",
              " ('carpenter', 0.46738606691360474),\n",
              " ('painter', 0.4598923623561859),\n",
              " ('restaurateur', 0.4586508572101593)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KVDAu5JbC3vB"
      },
      "source": [
        "Here other female-male stereotypes that you can explore. \n",
        "\n",
        "```\n",
        "nurse-doctor\n",
        "sewing-carpentry\n",
        "volleyball-football\n",
        "interior_designer-architect\n",
        "feminism-conservatism\n",
        "vocalist-guitarist\n",
        "diva-superstar\n",
        "cupcakes-pizzas\n",
        "hairdresser-barber\n",
        "```"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KDLn6n64C3vC"
      },
      "source": [
        "## 2.3 Gender direction\n",
        "\n",
        "Now, let's see an example of how binary meaning can be represented. Let's take our binary representation of the \"*gender*\" meaning.\n",
        "\n",
        "### $\\overrightarrow{she} - \\overrightarrow{he}$\n",
        " "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6Fl65I1vC3vC"
      },
      "source": [
        "from numpy.linalg import norm\n",
        "\n",
        "gender_direction = w2v_small['she'] - w2v_small['he']\n",
        "\n",
        "gender_direction /= norm(gender_direction)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-6uAKJvTC3vC",
        "outputId": "a6acef72-58ec-4277-f869-1fc196023611"
      },
      "source": [
        "# the @ operator returns the cosine similarity of 2 normalized vectors\n",
        "gender_direction @ w2v_small['architect']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-0.16785555"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "55MCmDhWC3vC",
        "outputId": "4e3c5209-d4e0-4924-8aad-1a9458a0de7a"
      },
      "source": [
        "gender_direction @ w2v_small['interior_designer']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.19714224"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KXoDjjFSC3vD"
      },
      "source": [
        "**⚡Interpret carefully: The word *architect* appears in more contexts with *he* than with *she*, and vice versa for *interior designer*.**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j6_SqDfEC3vD"
      },
      "source": [
        "In practice, we calculate the direction of the gender meaning subspace not only using \"he\" and \"she\", but using multiple pairs of words that represent each end of the meaning direction. This allows for better estimation, counterweighting the fact that words may have more than one meaning.\n",
        "\n",
        "- woman - man\n",
        "- girl - boy\n",
        "- she - he\n",
        "- mother - father\n",
        "- daughter - son\n",
        "- gal - guy\n",
        "- female - male\n",
        "- her - his\n",
        "- herself - himself\n",
        "- Mary - John\n",
        "\n",
        "Note that **this list of words is a very important input** of the analysis, as it defines the meaning that we are trying to explore!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HiqVsyXiC3vD"
      },
      "source": [
        "## 2.4 Try some words by yourself\n",
        "Now think about some word: where in the gender direction is it located? nearing which end, and how near?\n",
        "\n",
        "\n",
        "⚡ Keep in mind: You are performing exploratory data analysis, and not a systemic evaluation!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bvnuDrC7C3vD",
        "outputId": "3ffb45c2-16f8-4f9a-f502-cc8b6aa8cffd"
      },
      "source": [
        "gender_direction @ w2v_small['house']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.07210897"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VUjcCSnLpGI7",
        "outputId": "50bf9b2a-f8b9-4dc3-82e5-2baf9de66db9"
      },
      "source": [
        "gender_direction @ w2v_small['home']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-0.016742865"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9DuWu9yVC3vE"
      },
      "source": [
        "## 2.5 Projections in the gender direction"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bYdcit9fw9w_"
      },
      "source": [
        "Now we want to be more systematic in this exploration. \n",
        "\n",
        "We will be using a list of morphologically gender-neutral professions to do that. Gender-neutral words should be equally distant from both ends in the gender direction. We say that their bias should be around 0, and again we have a high-level construct to model that:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ucDbQ13uC3vE"
      },
      "source": [
        "from responsibly.we import GenderBiasWE\n",
        "\n",
        "w2v_small_gender_bias = GenderBiasWE(w2v_small, only_lower=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wOy5EDmRC3vE",
        "outputId": "cd9b1f37-ffae-4580-8ae8-ebc2f0bb43dc"
      },
      "source": [
        "w2v_small_gender_bias.positive_end, w2v_small_gender_bias.negative_end"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('she', 'he')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NzlmJWFdC3vE",
        "outputId": "f653d854-e229-4a7f-aa2a-5ac2b0ae8b53"
      },
      "source": [
        "# gender direction\n",
        "w2v_small_gender_bias.direction[:10]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-0.10056252, -0.09987912, -0.05384335,  0.00480521,  0.01521063,\n",
              "       -0.01666582,  0.00932552, -0.00625998, -0.11320096,  0.03992424])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KI8U2QhCC3vE"
      },
      "source": [
        "# list of gender neutral professions\n",
        "from responsibly.we.data import BOLUKBASI_DATA\n",
        "\n",
        "neutral_profession_names = BOLUKBASI_DATA['gender']['neutral_profession_names']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tAHD1RdXC3vF",
        "outputId": "8999853d-feb5-4c65-ff6c-a815613bf90b"
      },
      "source": [
        "neutral_profession_names[:8]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['accountant',\n",
              " 'acquaintance',\n",
              " 'actor',\n",
              " 'adjunct_professor',\n",
              " 'administrator',\n",
              " 'adventurer',\n",
              " 'advocate',\n",
              " 'aide']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "loqvR0qUC3vF"
      },
      "source": [
        "Note: Why `actor` is in the neutral profession names list while `actress` is not there?\n",
        "1. Due to the statistical nature of the method that is used to find the gender- specific and natural words\n",
        "2. That might be because `actor` nowadays is much more gender-neutral, compared to waiter-waitress (see [Wikipedia - The term Actress](https://en.wikipedia.org/wiki/Actor#The_term_actress))"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fLgkcOuGC3vF",
        "outputId": "e9ff166c-01d2-4a26-bceb-520561843ca8"
      },
      "source": [
        "len(neutral_profession_names)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "303"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qby3XDrfC3vF",
        "outputId": "c5ecf755-6396-4c7f-bc13-9abb35a88bff"
      },
      "source": [
        "# the same of using the @ operator on the bias direction\n",
        "\n",
        "w2v_small_gender_bias.project_on_direction(neutral_profession_names[0])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.008421647916552755"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "th7UryxdC3vF"
      },
      "source": [
        "Let's visualize the projections of professions (neutral and specific by the orthography) on the gender direction:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 615
        },
        "id": "juiycs76C3vF",
        "outputId": "5468783f-85fd-4b2c-a29f-076c3ed31cbf"
      },
      "source": [
        "import matplotlib.pylab as plt\n",
        "\n",
        "f, ax = plt.subplots(1, figsize=(10, 10))\n",
        "\n",
        "w2v_small_gender_bias.plot_projection_scores(n_extreme=20, ax=ax);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CUiw8Tjxx6Qh"
      },
      "source": [
        "They do not look very neutral, they edge towards one or the other end of the continuum."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K1MgiofyC3vF"
      },
      "source": [
        "EXTRA: Demo - Visualizing gender bias with [Word Clouds](http://wordbias.umiacs.umd.edu/)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1IlRykZjC3vG"
      },
      "source": [
        "The projections of occupation words on the gender direction are related to the real world!\n",
        "\n",
        "Garg, N., Schiebinger, L., Jurafsky, D., & Zou, J. (2018). Word embeddings quantify 100 years of gender and ethnic stereotypes. Proceedings of the National Academy of Sciences, 115(16), E3635-E3644."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oas2WYuTng5y"
      },
      "source": [
        "# Part 3: Hands on Mitigating Bias!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KwZmL19LC3vG"
      },
      "source": [
        "## 3.1 Direct Bias Measure\n",
        "\n",
        "Now that we have modelled the gender direction (with words representing each end) and we have a representation of how an unbiased word embedding would be (via gender-neutral words), we can diagnose bias, in a very simple, systematic way:\n",
        "\n",
        "1. Project each **neutral profession name** on the gender direction\n",
        "2. Calculate the absolute value of each projection\n",
        "3. Average it all"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MdXFI9EqC3vG",
        "outputId": "08f6f2df-79dd-4b13-c251-fa33fd3bdd18"
      },
      "source": [
        "# high-level function to calculate gender bias built into responsibly\n",
        "\n",
        "w2v_small_gender_bias.calc_direct_bias()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.07307905466893873"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MbB7U8IFC3vH",
        "outputId": "9d8fa66b-8449-42df-b1ad-094c672eeb77"
      },
      "source": [
        "# what responsibly does within the calc_direct_bias function:\n",
        "\n",
        "neutral_profession_projections = [w2v_small[word] @ w2v_small_gender_bias.direction\n",
        "                                  for word in neutral_profession_names]\n",
        "\n",
        "abs_neutral_profession_projections = [abs(proj) for proj in neutral_profession_projections]\n",
        "\n",
        "sum(abs_neutral_profession_projections) / len(abs_neutral_profession_projections)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0730790545759565"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dv_Zav4DC3vH"
      },
      "source": [
        "What are the assumptions of the direct bias measure? How does the choice of neutral words affect the definition of the bias? Can we measure **all** of the gender bias with this technique? \n",
        "\n",
        "An interesing property of this method is that it gives us a quantitative measure of the bias in the embedding, but keep in mind that this number only takes into consideration the bias present in the *assumed neutral* words with respect to the *defined gender direction*, which we agreed was a simplification of the actual phenomenon.\n",
        "\n",
        "What about words that are not in the list? Is this list comprehensive enough?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x2AxxNoXC3vH"
      },
      "source": [
        "## 3.2 Mitigating gender bias\n",
        "\n",
        "> We intentionally do not reference the resulting embeddings as \"debiased\" or free from all gender bias, and\n",
        "prefer the term \"mitigating bias\" rather that \"debiasing,\" to guard against the misconception that the resulting\n",
        "embeddings are entirely \"safe\" and need not be critically evaluated for bias in downstream tasks. \n",
        "\n",
        "<small>James-Sorenson, H., & Alvarez-Melis, D. (2019). [Probabilistic Bias Mitigation in Word Embeddings](https://arxiv.org/pdf/1910.14497.pdf). arXiv preprint arXiv:1910.14497.</small>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TlW6-I0vECj5"
      },
      "source": [
        "As an approach to mitigating bias, we will remove the gender projection from the words in the gender-neutral word list and then normalize.\n",
        "\n",
        "This method *needs* a list of gender neutral words to neutralize, or a list of words that are inherently gendered to not neutralize. \n",
        "\n",
        "For this particular example on gender bias, responsibly defines a list of neutral words and neutralizes those words. These words are defined as the complement of the morphologically gendered words, and they are quite a few in English, because English is an [analytic language](https://en.wikipedia.org/wiki/Analytic_language)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pxQfyJHNTnvM",
        "outputId": "b037caeb-f87e-47eb-fed8-783d4267c4ab"
      },
      "source": [
        "len(w2v_small_gender_bias._data['neutral_words'])"
      ],
      "execution_count": 201,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "26189"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 201
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NWuZsuaCSHCO",
        "outputId": "0894569a-e4ed-4ad7-d96c-d1732a0c06d4"
      },
      "source": [
        "import random\n",
        "print(random.sample(w2v_small_gender_bias._data['neutral_words'],10))"
      ],
      "execution_count": 200,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['sneaked', 'pipeline', 'involving', 'rodent', 'survived', 'affirmative_action', 'baptism', 'cushions', 'vibrancy', 'theoretical']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RzIzwzcXC3vH"
      },
      "source": [
        "# we obtain a version of the embeddings that is mitigated wrt the gender direction\n",
        "w2v_small_gender_debias = w2v_small_gender_bias.debias(method='neutralize', inplace=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ip3Xbn1JMh2D",
        "outputId": "8378d3aa-3318-40a3-8032-009709cd1c58"
      },
      "source": [
        "# measure of bias in the original embeddings\n",
        "w2v_small_gender_bias.calc_direct_bias()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.07307905479130264"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hFf08ZQmC3vI",
        "outputId": "8cf0c20f-540e-4bf6-ca50-12c30ebe2577"
      },
      "source": [
        "# measure of bias in the mitigated embeddings\n",
        "w2v_small_gender_debias.calc_direct_bias()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.1615064677713404e-09"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-sCYRrPPFz1-",
        "outputId": "3cd8cb4a-e924-4493-8044-7366d4c51bab"
      },
      "source": [
        "'home' in (w2v_small_gender_bias._data['neutral_words'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J3hgtHyxC3vH",
        "outputId": "f52b20ab-24bc-4efe-9d32-7ae1a4070eaa"
      },
      "source": [
        "print('home:',\n",
        "      'before =', w2v_small_gender_bias.model['home'] @ w2v_small_gender_bias.direction,\n",
        "      'after = ', w2v_small_gender_debias.model['home'] @ w2v_small_gender_debias.direction)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "home: before = 0.011340496779172263 after =  -1.176245451061858e-09\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SGQMLRoeApNg",
        "outputId": "44dd2b03-dd43-44d9-a0eb-5c2916d655bd"
      },
      "source": [
        "print('architect:',\n",
        "      'before =', w2v_small_gender_bias.model['architect'] @ w2v_small_gender_bias.direction,\n",
        "      'after = ', w2v_small_gender_debias.model['architect'] @ w2v_small_gender_debias.direction)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "architect: before = -0.17738309189742663 after =  -4.154597475580876e-10\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "in7w-yJ_CMG1",
        "outputId": "d3191b57-9e56-4f9b-d382-1e8d3d436c7b"
      },
      "source": [
        "print('interior_designer:',\n",
        "      'before =', w2v_small_gender_bias.model['interior_designer'] @ w2v_small_gender_bias.direction,\n",
        "      'after = ', w2v_small_gender_debias.model['interior_designer'] @ w2v_small_gender_debias.direction)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "interior_designer: before = 0.19845774284382506 after =  2.8877261946752195e-09\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MDIBHfJsNbVa",
        "outputId": "8f25065d-4b1a-4c70-dbdb-46e9025b9354"
      },
      "source": [
        "'man' in (w2v_small_gender_bias._data['neutral_words'])"
      ],
      "execution_count": 182,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 182
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IZC7y2I9C3vI",
        "outputId": "3e4c94af-db0a-4c2a-cfc5-2035864a15d2"
      },
      "source": [
        "print('man:',\n",
        "      'before =', w2v_small_gender_bias.model['man'] @ w2v_small_gender_bias.direction,\n",
        "      'after = ', w2v_small_gender_debias.model['man'] @ w2v_small_gender_debias.direction)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "man: before = -0.22095192936041713 after =  -0.2209519293604172\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mPh777kyC3vI",
        "outputId": "d25662d6-c581-45e1-e0ed-389d1e4a8e4a"
      },
      "source": [
        "print('woman:',\n",
        "      'before =', w2v_small_gender_bias.model['woman'] @ w2v_small_gender_bias.direction,\n",
        "      'after = ', w2v_small_gender_debias.model['woman'] @ w2v_small_gender_debias.direction)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "woman: before = 0.3403474745246814 after =  0.3403474745246813\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 621
        },
        "id": "G3sd0hLUC3vI",
        "outputId": "6db3889a-76bc-493e-8f5d-5a26b2b80d36"
      },
      "source": [
        "f, ax = plt.subplots(1, figsize=(10, 10))\n",
        "\n",
        "w2v_small_gender_debias.plot_projection_scores(n_extreme=20, ax=ax);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OCJLjMTsvR6Y"
      },
      "source": [
        "If you liked this exploration on gender bias, maybe you will want to take a look at an exploration on racial bias in the [original notebook](https://colab.research.google.com/github/ResponsiblyAI/word-embedding/blob/main/tutorial-bias-word-embedding.ipynb)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c90opOeK-YuJ"
      },
      "source": [
        "# Parte 4: Ahora en castellano!\n",
        "\n",
        "Vamos a ver ahora cómo se trasladan estas técnicas a otro idioma.\n",
        "\n",
        "El castellano es una lengua con varias diferencias con respecto al inglés:\n",
        "- la mayoría de las palabras tienen **género marcado explícitamente en su forma**. Por lo tanto, será difícil obtener una lista de palabras neutras con respecto al género, que responsibly define muy fácilmente para el inglés.\n",
        "- se pueden **omitir los sujetos** de las oraciones, por lo tanto, las ocurrencias de \"*él*\" y \"*ella*\" tendrán una distribución muy diferente que las de \"*he*\" y \"*she*\".\n",
        "\n",
        "Con estas diferencias ¿resultarán efectivas las estrategias desarrolladas para el inglés?\n",
        "\n",
        "Vamos a analizar los [word embeddings para el castellano](http://dcc.uchile.cl/~jperez/word-embeddings/fasttext-sbwc.vec.gz) obtenidos mediante [fastText](https://fasttext.cc/) a partir del [Spanish Billion Words Corpus](https://github.com/crscardellino/sbwce). Este embedding representa 855.380 palabras en 300 dimensiones, obteniendo sus contextos de ocurrencia a partir de 1'4 billones de palabras. Para este ejemplo usaremos solamente las 100000 palabras más frecuentes, así se ejecutará todo más rápido.\n",
        "\n",
        "A diferencia de los embeddings obtenidos con word2vec, los embeddings obtenidos con fastText procesan también cadenas de caracteres dentro de las palabras, lo cual es muy adecuado para modelar lenguas donde las palabras tienen muchas variaciones en la forma, como el castellano, que es una [lengua de tipología fusionante](https://es.wikipedia.org/wiki/Lengua_fusionante). Efectivamente, mientras que en inglés (una [lengua aislante](https://es.wikipedia.org/wiki/Lengua_aislante)) las palabras tienen pocas formas diferentes, en castellano las palabras tienen muchas, porque significados como género, número, o tiempo verbal se expresan como variaciones en la forma de la palabra: \"*escribirá, escribiremos, escribirían*\".\n",
        "\n",
        "Empecemos por obtener datos y librerías."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rnNcdmBU-emQ",
        "outputId": "030ddb27-7f11-43e7-fdb0-5ebba10f9f56"
      },
      "source": [
        "*# Descargar el modelo e importar librerías necesarias\n",
        "import numpy as np\n",
        "import logging\n",
        "\n",
        "from numpy.linalg import norm\n",
        "from gensim.models import KeyedVectors\n",
        "from sklearn.metrics import classification_report, accuracy_score\n",
        "\n",
        "logging.basicConfig(format='%(asctime)s : %(message)s', level=logging.INFO)\n",
        "\n",
        "!wget -q http://dcc.uchile.cl/~jperez/word-embeddings/fasttext-sbwc.100k.vec.gz\n",
        "\n",
        "!gzip -d -q fasttext-sbwc.100k.vec.gz\n",
        "!ls"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "fasttext-sbwc.100k.vec\tsample_data\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tBIhi_aM-glm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "885c7f91-2ab7-4370-d83b-c5f97c9cff62"
      },
      "source": [
        "we = KeyedVectors.load_word2vec_format('fasttext-sbwc.100k.vec')\n",
        "# Esto normaliza los vectores\n",
        "we.init_sims(replace=True)"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2021-06-15 22:24:13,448 : loading projection weights from fasttext-sbwc.100k.vec\n",
            "2021-06-15 22:24:45,186 : loaded (100000, 300) matrix from fasttext-sbwc.100k.vec\n",
            "2021-06-15 22:24:45,190 : precomputing L2-norms of word weight vectors\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SL8LuKQd-kFE"
      },
      "source": [
        "# definimos cuándo dos vectores son \"casi iguales\" en este embedding\n",
        "\n",
        "from numpy.testing import assert_almost_equal\n",
        "\n",
        "length_vectors = norm(we.vectors_norm, axis=1)\n",
        "\n",
        "assert_almost_equal(actual=length_vectors,\n",
        "                    desired=1,\n",
        "                    decimal=5)"
      ],
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WvOvYHV9-3G1"
      },
      "source": [
        "## 4.1 Palabras con estereotipo de género\n",
        "\n",
        "En castellano casi todas las palabras tienen género morfológico explícito, así que vamos a centrarnos directamente en las que reflejan estereotipos de género."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MjGqBHdNyk3M"
      },
      "source": [
        "Vemos que la aritmética de vectores produce unos resultados bastante comparables a los del inglés."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0OKYIQWd-wh-",
        "outputId": "3e6e9909-6ecb-4b88-e9da-9b0abf704f22"
      },
      "source": [
        "we.most_similar(positive=['doctor'],negative=['mujer'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('dr', 0.4176639914512634),\n",
              " ('licenciado', 0.3791270852088928),\n",
              " ('doctores', 0.3671714663505554),\n",
              " ('honoris', 0.3577732443809509),\n",
              " ('professor', 0.34742414951324463),\n",
              " ('neurólogo', 0.3422521650791168),\n",
              " ('cirujano', 0.33602601289749146),\n",
              " ('traumatólogo', 0.3323988914489746),\n",
              " ('profesor', 0.33126506209373474),\n",
              " ('cardiólogo', 0.3256565034389496)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fpbRUklH-xsM",
        "outputId": "74d64371-7424-45fa-a806-3d6f4960bc8b"
      },
      "source": [
        "we.most_similar(positive=['doctora'],negative=['hombre'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('psicóloga', 0.43664997816085815),\n",
              " ('bióloga', 0.40995094180107117),\n",
              " ('catedrática', 0.40756943821907043),\n",
              " ('profesora', 0.40630972385406494),\n",
              " ('historiadora', 0.38048917055130005),\n",
              " ('investigadora', 0.3678087592124939),\n",
              " ('licenciada', 0.366742342710495),\n",
              " ('vicerrectora', 0.3654743432998657),\n",
              " ('filóloga', 0.36261123418807983),\n",
              " ('ginecóloga', 0.35758891701698303)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OcL2i10u-0L3"
      },
      "source": [
        "A pesar de las diferencias entre el castellano y el inglés, podemos ver en la exploración del embedding fenómenos comparables. \n",
        "\n",
        "Se evidencian nociones estereotípicas sobre especialidades médicas, asociando a las doctoras con ginecología, obstetricia, enfemería, mientras que los doctores están mas relacionados con profesor, investigador, catedrático...\n",
        "\n",
        "Podemos seguir explorando cómo el uso de las palabras refleja estereotipos."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JuQQf3HR-8t6",
        "outputId": "7dd244e6-8bc5-45c4-a85f-5fb94eb730ff"
      },
      "source": [
        "we.most_similar(positive=['inteligente', 'mujer'], negative=['hombre'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('sensata', 0.4874057173728943),\n",
              " ('talentosa', 0.48738622665405273),\n",
              " ('innovadora', 0.47544434666633606),\n",
              " ('manipuladora', 0.4719089865684509),\n",
              " ('emprendedora', 0.46958673000335693),\n",
              " ('atractiva', 0.45731809735298157),\n",
              " ('extrovertida', 0.4571165144443512),\n",
              " ('impulsiva', 0.44972628355026245),\n",
              " ('educada', 0.447437047958374),\n",
              " ('ingeniosa', 0.44558414816856384)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n7AzXQQi-__9",
        "outputId": "683d6806-bbe1-4601-de31-b6c588afaefc"
      },
      "source": [
        "we.most_similar(positive=['inteligente', 'hombre'], negative=['mujer'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('astuto', 0.5987648963928223),\n",
              " ('inteligentemente', 0.565445601940155),\n",
              " ('inteligentes', 0.5556885004043579),\n",
              " ('intuitivo', 0.5533121824264526),\n",
              " ('visionario', 0.5499666929244995),\n",
              " ('bondadoso', 0.5497375726699829),\n",
              " ('afable', 0.5486626625061035),\n",
              " ('disciplinado', 0.5481559634208679),\n",
              " ('honesto', 0.5480844974517822),\n",
              " ('ingenioso', 0.5475456714630127)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lKDZb1_i3TPH",
        "outputId": "9c3e8d30-69dc-47f0-c6ec-c944e55b2782"
      },
      "source": [
        "we.most_similar(positive=['médico','mujer'], negative=['hombre'])"
      ],
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('ginecóloga', 0.6046308279037476),\n",
              " ('obstetra', 0.5925636887550354),\n",
              " ('pediatra', 0.5792399048805237),\n",
              " ('ginecológica', 0.5755670070648193),\n",
              " ('obstetricia', 0.562862753868103),\n",
              " ('enfermera', 0.5607845783233643),\n",
              " ('médica', 0.5571267604827881),\n",
              " ('médicos', 0.5507985353469849),\n",
              " ('ginecología', 0.5502138137817383),\n",
              " ('obstétrica', 0.5493755340576172)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VBpmvqVn3k9t",
        "outputId": "fa8e0642-381a-46a8-de6e-a6833b352318"
      },
      "source": [
        "we.most_similar(positive=['médico','hombre'], negative=['mujer'])"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('cardiólogo', 0.596772313117981),\n",
              " ('cirujano', 0.5966023802757263),\n",
              " ('traumatólogo', 0.5808753967285156),\n",
              " ('medico', 0.5804007053375244),\n",
              " ('galeno', 0.5700283050537109),\n",
              " ('ginecólogo', 0.5457277297973633),\n",
              " ('neurólogo', 0.54304438829422),\n",
              " ('enfermero', 0.5366503000259399),\n",
              " ('psiquiatra', 0.5364155173301697),\n",
              " ('neurocirujano', 0.5363881587982178)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G1bLRo2R3WIo",
        "outputId": "cb891567-2262-4a08-95a7-b5bd70fddd11"
      },
      "source": [
        "we.most_similar(positive=['médica','hombre'], negative=['mujer'])"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('médico', 0.5506406426429749),\n",
              " ('medica', 0.5366383790969849),\n",
              " ('médicas', 0.5251321196556091),\n",
              " ('clínica', 0.5138856172561646),\n",
              " ('médicos', 0.499112069606781),\n",
              " ('medico', 0.49768391251564026),\n",
              " ('quirúrgica', 0.49031704664230347),\n",
              " ('galeno', 0.4826927185058594),\n",
              " ('odontológica', 0.48202377557754517),\n",
              " ('hospitalaria', 0.4809926748275757)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CUmuIa8p3uog",
        "outputId": "4d26aa4c-3e63-43fa-dccd-e9ba5ed20645"
      },
      "source": [
        "we.most_similar(positive=['médica','mujer'], negative=['hombre'])"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('ginecológica', 0.6155674457550049),\n",
              " ('obstétrica', 0.5944493412971497),\n",
              " ('obstetricia', 0.5862675905227661),\n",
              " ('pediátrica', 0.5625762939453125),\n",
              " ('ginecología', 0.5593430995941162),\n",
              " ('obstétricos', 0.5461073517799377),\n",
              " ('medica', 0.5453529357910156),\n",
              " ('ambulatoria', 0.5365152955055237),\n",
              " ('obstétricas', 0.5328614115715027),\n",
              " ('odontológica', 0.5309116244316101)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GBYi2Zmk_rCX"
      },
      "source": [
        "## 4.2 Dirección de género\n",
        "\n",
        "Ahora vamos a construir el espacio de género! Empecemos como en inglés, con \"*él*\" y \"*ella*\"."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KOeDVU_O-Rnp",
        "outputId": "57e17d43-d8ff-4520-ab0b-a504dce9a647"
      },
      "source": [
        "from responsibly.we import BiasWordEmbedding\n",
        "\n",
        "we_genero = BiasWordEmbedding(we, only_lower=True)"
      ],
      "execution_count": 202,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2021-06-16 01:19:35,771 : precomputing L2-norms of word weight vectors\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BICdy7KN4VVN"
      },
      "source": [
        "espacio_f = ['ella']\n",
        "espacio_m = ['él']\n",
        "\n",
        "we_genero._identify_direction('Femenino', 'Masculino',\n",
        "                                          definitional=(espacio_f, espacio_m),\n",
        "                                          method='sum')"
      ],
      "execution_count": 203,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nak_NBgr_Oym"
      },
      "source": [
        "gender_direction = we_genero.direction\n",
        "\n",
        "gender_direction /= norm(gender_direction)"
      ],
      "execution_count": 204,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lih6vMnE_Ru7",
        "outputId": "4385f305-cd9b-48a1-b6da-181bc248ef30"
      },
      "source": [
        "gender_direction @ we.word_vec('arquitecta')"
      ],
      "execution_count": 205,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.21170145"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 205
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uo_k9PPJ_THl",
        "outputId": "fe845ca4-4d9b-4193-b9ce-51770d23d8a6"
      },
      "source": [
        "gender_direction @ we.word_vec('arquitecto')"
      ],
      "execution_count": 206,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-0.09577321"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 206
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "atAX_zxi43Ys"
      },
      "source": [
        "Pues no parece que se diferencien mucho las palabras...\n",
        "\n",
        "⚡ El castellano es una [lengua que permite la omisión del sujeto](https://en.wikipedia.org/wiki/Pro-drop_language), por lo tanto **\"*él*\" y \"*ella*\" ocurren menos veces y en contextos muy distintos a \"*he*\" y \"*she*\". Parece que no son buenos delimitadores del subespacio de género!**\n",
        "\n",
        "En ese caso, usemos más y mejores indicadores."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DJfWPEO--U6E"
      },
      "source": [
        "espacio_f = ['mujer', 'ella' , 'chica', 'niña', 'esposa', 'señora', 'hermana', 'madre', 'abuela']\n",
        "espacio_m = ['hombre', 'él', 'chico', 'niño', 'esposo', 'señor', 'hermano', 'padre','abuelo']\n",
        "\n",
        "we_genero._identify_direction('Femenino', 'Masculino',\n",
        "                                          definitional=(espacio_f, espacio_m),\n",
        "                                          method='sum')"
      ],
      "execution_count": 207,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wcwQwQFV6PBV"
      },
      "source": [
        "gender_direction = we_genero.direction\n",
        "\n",
        "gender_direction /= norm(gender_direction)"
      ],
      "execution_count": 208,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ks37gqji7HNg",
        "outputId": "5f77391d-dd4c-41bd-9f89-f318daf5e15c"
      },
      "source": [
        "gender_direction @ we.word_vec('arquitecta')"
      ],
      "execution_count": 209,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.29372594"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 209
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O6ujmB5L7I_z",
        "outputId": "15c93494-97a9-46a3-c2d9-f5ff6fbdc019"
      },
      "source": [
        "gender_direction @ we.word_vec('arquitecto')"
      ],
      "execution_count": 210,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-0.19526821"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 210
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FU80SdDw7ThK"
      },
      "source": [
        "Ahora bien: cómo sabemos que estas palabras no se están situando en diferentes posiciones del subespacio de género por su género morfológico, y no porque estén representando estereotipos?\n",
        "\n",
        "Hagamos el experimento con palabras que tengan el mismo género morfológico pero que representen diferentes estereotipos de género."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kxajoveD_Unq",
        "outputId": "0837dabe-7753-4b3e-d45b-f0e8c04ad64a"
      },
      "source": [
        "gender_direction @ we.word_vec('arquitectura')"
      ],
      "execution_count": 211,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.01069098"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 211
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x-_pf8u9_V6s",
        "outputId": "6dff4ec0-ead5-402d-df6f-c03a86670c97"
      },
      "source": [
        "gender_direction @ we.word_vec('ingeniería')"
      ],
      "execution_count": 212,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-0.06690517"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 212
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2CJGOHQs7qSl",
        "outputId": "6e8ca6ce-d1ef-44c5-b31b-f2301928f595"
      },
      "source": [
        "gender_direction @ we.word_vec('obstetricia')"
      ],
      "execution_count": 213,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.06260888"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 213
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FI9grDMc7329",
        "outputId": "12cf8086-1585-488f-9b94-16a8e8d4c442"
      },
      "source": [
        "gender_direction @ we.word_vec('medicina')"
      ],
      "execution_count": 214,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-0.02380787"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 214
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C5d9ZZRl9uxh"
      },
      "source": [
        "Ambas palabras tienen género gramatical femenino, pero\n",
        "parecen ubicarse del lado masculino."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MzDFVhEt_dBD"
      },
      "source": [
        "Podríamos pensar que la posición en la dirección del género en castellano solamente tiene que ver con el género gramatical de la palabra, pero no parece ser así!\n",
        "\n",
        "Por lo tanto, parece que hemos podido llegar a una representación de la dirección de género que es indicativa de estereotipos de género.\n",
        "\n",
        "Hagamos una última prueba con uno de los ejemplos que vimos para el inglés: \"*house*\" y \"*home*\"."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JGS7skfV8wYJ",
        "outputId": "7cefc955-f588-4b92-da4e-c227ad6d7330"
      },
      "source": [
        "gender_direction @ we.word_vec('casa')"
      ],
      "execution_count": 215,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.036148656"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 215
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nGXAR3Ef81kf",
        "outputId": "991c6f6a-e018-4d88-e451-d20ade3a99e2"
      },
      "source": [
        "gender_direction @ we.word_vec('hogar')"
      ],
      "execution_count": 216,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-0.030880773"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 216
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W-F3scUx88az"
      },
      "source": [
        "Aunque la dirección de género parece funcionar, ¿quizás los estereotipos asociados a las palabras no son directamente comparables? ¡Esto amerita un estudio de corpus!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UyhIbwRV_BM3"
      },
      "source": [
        "## 4.3 Medición directa del sesgo de género"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xtx_IaYAIWEW"
      },
      "source": [
        "Como en inglés, podemos medir el sesgo en esta dirección de género usando una lista de palabras que sean neutras con respecto al género, es decir, que no tengan género morfológicamente marcado. \n",
        "\n",
        "La librería responsibly provee una lista de profesiones neutras en inglés. En español, la lista de profesiones neutras es mucho más corta porque en castellano la mayor parte de las palabras tienen género morfológicamente marcado. \n",
        "\n",
        "Sin embargo, hay algunas profesiones que tienen forma no marcada con género, principalmente palabras terminadas en -ista, -nte. \n",
        "\n",
        "Con esta lista, la estrategia desarrollada para el inglés debería funcionar."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b1jjG0iU_IX3"
      },
      "source": [
        "profesiones_neutras = [\n",
        "    'chofer',\n",
        "    'columnista',\n",
        "    'publicista',\n",
        "    'naturista',\n",
        "    'asistente',\n",
        "    'taxista',\n",
        "    'psiquiatra',\n",
        "    'policia',\n",
        "    'dentista',\n",
        "    'florista',\n",
        "    'docente',\n",
        "    'periodista',\n",
        "    'electricista',\n",
        "    'economista',\n",
        "    'atleta',\n",
        "    'terapeuta',\n",
        "    'piloto',\n",
        "    'modelo',\n",
        "    'estudiante',\n",
        "    'comerciante',\n",
        "    'chef',\n",
        "    'cantante',\n",
        "    'militar',\n",
        "]"
      ],
      "execution_count": 217,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YUXikrE-YWtc"
      },
      "source": [
        "Para medir el sesgo usamos esta lista de profesiones neutras de la siguiente forma:\n",
        "\n",
        "1. Proyectamos cada uno de los nombres de profesión neutros en la dirección de género\n",
        "2. Calculamos el valor absoluto de cada proyección\n",
        "3. Lo promediamos"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 621
        },
        "id": "04EVnPAI_K_2",
        "outputId": "9876bef3-04d5-4671-ad66-4659de80ac87"
      },
      "source": [
        "import matplotlib.pylab as plt\n",
        "\n",
        "f, ax = plt.subplots(1, figsize=(10, 10))\n",
        "\n",
        "we_genero.plot_projection_scores(\n",
        "    profesiones_neutras + espacio_f[:1] + espacio_m[:1],\n",
        "    n_extreme=20,\n",
        "    ax=ax);"
      ],
      "execution_count": 218,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HPjUd3G__xHt",
        "outputId": "12c7e8c2-d881-47ac-8c2d-64e649824d46"
      },
      "source": [
        "we_genero.calc_direct_bias(profesiones_neutras, c=None)"
      ],
      "execution_count": 219,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.06407627389442337"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 219
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xLFsDvD_AY_4"
      },
      "source": [
        "## 4.4 Mitigación del sesgo de género"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Knqk1IOcCjqY"
      },
      "source": [
        "La estrategia que vimos para mitigar sesgo de género consiste en eliminar la proyección de género de las palabras de una lista de palabras neutras, y después normalizar. \n",
        "\n",
        "El problema que tenemos para trasladar esta estrategia directamente al castellano es que la lista de palabras neutras es mucho más difícil de obtener que para el inglés. En inglés la lista de palabras neutras tenía más de 26000 palabras, porque se definía como el complemento de la lista de palabras con género morfológicamente marcado, que son muy pocas en inglés y por lo tanto sencillas de listar.\n",
        "\n",
        "Como hemos dicho, en castellano la mayor parte de palabras tienen género morfológico marcado, así que resultará difícil construir la lista de palabras neutras.\n",
        "\n",
        "¿Qué pasa si la lista de palabras neutras es pequeña?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vblZ7p8YbiZk"
      },
      "source": [
        "### Lista de palabras neutras corta\n",
        "\n",
        "Veamos qué pasa si usamos solamente las palabras de profesiones neutras como lista de palabras neutras para mitigar el sesgo. Esta lista tiene muy pocas palabras."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eAX5RUffbseG",
        "outputId": "61f54de3-4125-40b7-f9d8-217f2d9b8e79"
      },
      "source": [
        "len(profesiones_neutras)"
      ],
      "execution_count": 224,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "23"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 224
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZYQDM_rMAoE0",
        "outputId": "775cc6aa-26be-4543-b54c-a6d47a21b405"
      },
      "source": [
        "we_genero_debias = we_genero.debias(method = 'neutralize' , neutral_words= profesiones_neutras, inplace=False)\n"
      ],
      "execution_count": 225,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2021-06-16 01:28:34,401 : precomputing L2-norms of word weight vectors\n",
            "2021-06-16 01:28:36,214 : precomputing L2-norms of word weight vectors\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6GvXytuHUmi1"
      },
      "source": [
        "w2v_small_gender_debias = w2v_small_gender_bias.debias(method='neutralize', inplace=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2IYxldUGb98f"
      },
      "source": [
        "Si medimos el sesgo después de la mitigación vemos que se ha reducido muchísimo! y el gráfico también indica lo mismo."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aEd6e8iSGvak",
        "outputId": "48461f12-3ccd-4b7e-fa70-a2c6643a98a6"
      },
      "source": [
        "we_genero_debias.calc_direct_bias(profesiones_neutras, c=None)"
      ],
      "execution_count": 226,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8.23345780925669e-09"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 226
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 621
        },
        "id": "ljWRBRJYAqGk",
        "outputId": "c2e285ac-6009-41d5-f259-77265878ad4c"
      },
      "source": [
        "f, ax = plt.subplots(1, figsize=(10, 10))\n",
        "\n",
        "we_genero_debias.plot_projection_scores(\n",
        "    profesiones_neutras + espacio_f[:1] + espacio_m[:1],\n",
        "    n_extreme=20,\n",
        "    ax=ax);"
      ],
      "execution_count": 222,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VESACLCqcLDO"
      },
      "source": [
        "Vamos a hacer una exploración cualitativa de cómo se ha reducido el sesgo:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D3gBbj_kdDQT",
        "outputId": "aa44c5b8-9b19-4a41-c8c5-c748dcb387bf"
      },
      "source": [
        "print('estudiante:',\n",
        "      'antes =', we_genero.model['estudiante'] @ we_genero.direction,\n",
        "      'después = ', we_genero_debias.model['estudiante'] @ we_genero_debias.direction)"
      ],
      "execution_count": 229,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "estudiante: antes = -0.0048034997 después =  -1.2823875e-10\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m6heBa7HdJgl"
      },
      "source": [
        "Pero, ¿qué pasa con las palabras que no están en la lista?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H9v2z7GpL8tw",
        "outputId": "8e3aa5c7-0999-4052-fd80-21199dc85f89"
      },
      "source": [
        "print('ingeniero:',\n",
        "      'antes =', we_genero.model['ingeniero'] @ we_genero.direction,\n",
        "      'después = ', we_genero_debias.model['ingeniero'] @ we_genero_debias.direction)"
      ],
      "execution_count": 227,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "ingeniero: antes = -0.24747832 después =  -0.24747832\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zezaw_S1cZqJ",
        "outputId": "2eb5a147-b544-4db8-e94c-1d03dba64062"
      },
      "source": [
        "print('laboralista:',\n",
        "      'antes =', we_genero.model['laboralista'] @ we_genero.direction,\n",
        "      'después = ', we_genero_debias.model['laboralista'] @ we_genero_debias.direction)"
      ],
      "execution_count": 228,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "laboralista: antes = -0.08559768 después =  -0.08559768\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aJJJwxUYfU-j"
      },
      "source": [
        "Esta aproximación sólo elimina el sesgo de las palabras que pongamos en la lista! \n",
        "\n",
        "Pero si en el castellano la mayoría de palabras tienen género marcado morfológicamente ¿qué palabras podemos poner en una lista de palabras neutras con respecto al género?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SNiGG6yUFowb"
      },
      "source": [
        "## 4.5 Palabras de género semántico neutro para el castellano"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gO_eaAPvdQEC"
      },
      "source": [
        "\n",
        "* Palabras de categorías gramaticales sin género: verbos, adverbios, preposiciones\n",
        "* Palabras abstractas, cuyo género morfológico no tiene una correspondencia con un género semántico\n",
        "* Palabras de colectivos, igual\n",
        "* Más palabras sin género marcado morfológicamente, como las terminadas en -ble, -nte\n",
        "\n",
        "Ahora bien, si estas palabras no tienen género semántico, quizás no nos sirven para mitigar sesgo porque no están sesgadas... ¿o sí?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Uomy6za9fgJj"
      },
      "source": [
        "### Categorías gramaticales sin género"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bd8lf0ooflYW"
      },
      "source": [
        "verbos = [ \n",
        "          'comprar',\n",
        "          'vender',\n",
        "          'dormir',\n",
        "          'despertar',\n",
        "          'soñar',\n",
        "          'llorar',\n",
        "          'gritar',\n",
        "          'hablar',\n",
        "          'preguntar',\n",
        "          'pensar',\n",
        "          'inventar',\n",
        "          'bailar',\n",
        "          'cantar',\n",
        "          'cocinar',\n",
        "          'sentir',\n",
        "          'bordar',\n",
        "          'tejer',\n",
        "          'coser',\n",
        "          'razonar',\n",
        "          'argumentar',\n",
        "          'cursar',\n",
        "          'programar'\n",
        "]"
      ],
      "execution_count": 257,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 621
        },
        "id": "KNmnHEkcf71t",
        "outputId": "28bd21db-7200-4579-d86a-d4b40b37ca14"
      },
      "source": [
        "f, ax = plt.subplots(1, figsize=(10, 10))\n",
        "\n",
        "we_genero.plot_projection_scores(\n",
        "    verbos + ['hombre'] + ['mujer'],\n",
        "    n_extreme=20,\n",
        "    ax=ax);"
      ],
      "execution_count": 258,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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JM40rkJIkSWrFAClJkqRWDJCSJElqxQApSZKkVgyQkiRJamXGB8gkC5OsmG5zSZIkDaoZHyC3RJKttnC8r0GSJEmzzqAEyK2TnJbk2iRfSbJdkt9PsizJ8iSfT/IwgCQ3JvlIkiuAI5Psl+SqJFcBb9wwYbMaeUGSK5qvA5v2Q5r2M4H/7MvZSpIk9dGgBMgnAydW1VOAu4C/BE4Cjqqqfei+MP0NPf1vq6pFVfVl4AvAm6rqaaPmvBV4XlUtAo4CPtmzbxHw5qp60uhCkixOMpxkeGRkZIJOT5IkafoYlAB5c1Vd2Gx/Efh94Iaq+nHTdjLw7J7+pwMkmQ/Mr6ofNu2n9vTZBvhckuXAGcCePfsuq6obxiqkqpZUVaeqOkNDQ1t0UpIkSdPRoNzDV6M+rwJ2fpD+a8Yx51uBXwJPoxu017YcL0mSNJAGZQVyQZJnNtsvB4aBhUme2LS9Ejh/9KCqWgWsSnJQ03R0z+55wM+ran0zfoseuJEkSRoUgxIgrwfemORaYCfg48CrgTOaS9Drgc9sZOyrgX9JciWQnvYTgVc1D9fsgauOkiRJAKRq9NVfTZROp1PDw8P9LkPSNHfrqtX9LuFXHjF/br9LkNQnSZZWVWc8fQdlBVKSJElTxAApSZKkVgyQkiRJamVQXuMjSTOW9x1KmmlcgZQkSVIrBkhJkiS1YoCUJElSKwZISZIkteJDNJLUZ3euvmdC5pk3d7sJmUeSNsUVSEmSJLVigJQkSVIrBkhJkiS1YoCUJElSKwZISZIktTIrA2SSi/pdgyRJ0kw1KwNkVR04EfMk8TVIkiRp1pmRATLJMUmuTnJVklOTLExybtN2TpIFTb9dk3y96XdVkgOb9tXN90OSnJfkK0muS3JakjT7jktyeZIVSZb0tJ+X5BNJhoE39+lHIEmS1DczLkAm2Qt4N/Ccqnoa3RD3z8DJVfVU4DTgk033TwLnN/0WAdeMMeXTgbcAewK/Azyraf9UVe1fVXsD2wKH9Yx5aFV1quqjY9S3OMlwkuGRkZEtPV1JkqRpZ8YFSOA5wBlVtRKgqm4Hngl8qdl/KnBQT99PN/3WVdWdY8x3WVX9b1WtB64EFjbthya5NMnyZp69esacvrHiqmpJEy47Q0NDm3WCkiRJ05n38MF9PdvrgK2TzAFOBDpVdXOS44E5Pf3WTGF9kiRJ08pMXIE8Fzgyyc4ASR4OXAS8rNl/NHBBs30O8Iam31ZJ5o3zGBvC4sokc4EjJqJwSZKkQTDjViCr6pokHwDOT7IOWAa8CfhCkncAI8Crm+5vBpYkeS3d1cU3ABeP4xirknwOWAH8Arh84s9EkiRpZkpV9buGgdXpdGp4eLjfZUia5u5cfc+EzDNv7nYTMo+k2SnJ0qrqjKfvTLyELUmSpD4yQEqSJKkVA6QkSZJamXEP0UjSoPHeRUkzjSuQkiRJasUAKUmSpFYMkJIkSWrFeyAlqc/W3HPvJvtsv922U1CJJI2PK5CSJElqxQApSZKkVgyQkiRJasUAKUmSpFYMkJIkSWrFANlIMj/Jn/W7DkmSpOnOAPlr84FWATJd/gwlSdKsMjDhJ8kxSa5OclWSU5McnuTSJMuSfD/Jrk2/45N8Psl5SX6a5C+aKT4MPCHJlUlOSDI3yTlJrkiyPMmLmvELk1yf5BRgBbBbf85YkiSpPwbiReJJ9gLeDRxYVSuTPBwo4HerqpL8X+CvgLc1Q/YADgV2AK5P8mngncDeVbVvM+fWwJ9U1V1JdgEuSXJmM3534FVVdckYtSwGFgMsWLBgks5YkiSpfwYiQALPAc6oqpUAVXV7kn2A05M8CngocENP/29X1X3AfUluBXYdY84AH0zybGA98JiefjeNFR6bYy8BlgB0Op3a8lOTJEmaXgbmEvYY/hn4VFXtA7wOmNOz776e7XWMHaSPBoaA/ZpVyV/2zLFm4suVJEmaGQYlQJ4LHJlkZ4DmEvY84GfN/leNY4676V7S3mAecGtV3Z/kUOBxE1ivJEnSjDUQl7Cr6pokHwDOT7IOWAYcD5yR5A66AfPxm5jjtiQXJlkBfAf4CPDNJMuBYeC6yTwHSZKkmSJV3qY3WTqdTg0PD/e7DEnT3Jp77t1kn+2323YKKpE0myVZWlWd8fQdlEvYkiRJmiIGSEmSJLVigJQkSVIrA/EQjSTNZN7fKGmmcQVSkiRJrRggJUmS1IoBUpIkSa0YICVJktSKD9FIUp/5InFJM40rkJIkSWrFAClJkqRWDJCSJElqxQApSZKkVgyQkiRJasUAKUmSpFZmdYBMskWvMdrS8ZIkSTPRwATIJMckuTrJVUlOTXJSkiN69q9uvh+S5IIkZwL/mWT7JN9uxq1IclTT77gklzdtS5KkaT8vySeSDANv7se5SpIk9dNArKAl2Qt4N3BgVa1M8nDgYw8yZBGwd1XdkOQlwC1V9cJmrnlNn09V1d83bacChwHfbPY9tKo6G6llMbAYYMGCBVt4ZpIkSdPPoKxAPgc4o6pWAlTV7Zvof1lV3dBsLweel+QjSQ6uqjub9kOTXJpkeTP/Xj3jT9/YxFW1pKo6VdUZGhravLORJEmaxgYlQI7lAZrzS/IQ4KE9+9Zs2KiqH9NdkVwOvL+5dD0HOBE4oqr2AT4HzBlrvCRJ0mwzKAHyXODIJDsDNJewbwT2a/b/MbDNWAOTPBq4p6q+CJxAN0xuCIsrk8wFjhhrrCRJ0mw0EPdAVtU1ST4AnJ9kHbAM+GvgG0muAr7LxlcN9wFOSLIeuB94Q1WtSvI5YAXwC+DyST8JSZKkGSJV1e8aBlan06nh4eF+lyFpmltzz72b7LP9dttOQSWSZrMkSzf2kPBog3IJW5IkSVPEAClJkqRWDJCSJElqZSAeopGkmcz7GyXNNK5ASpIkqRUDpCRJkloxQEqSJKkVA6QkSZJa8SEaSdpMa++6Y0LmmbPjThMyjyRNFVcgJUmS1IoBUpIkSa0YICVJktSKAVKSJEmtGCAlSZLUysAFyCTHJ3n7JMz7H0nmT/S8kiRJM82sfo1Pkq2r6oHx9K2qPxpjfIBU1foJL06SJGmaGogVyCTvSvLjJD8Cnty07ZvkkiRXJ/l6kp2a9vOSfCLJMPDmJCcl+XTT96dJDkny+STXJjmp5xg3JtklycIk1yc5BVgB7NaHU5YkSeqbGR8gk+wHvAzYF/gjYP9m1ynAX1fVU4HlwHt6hj20qjpV9dHm807AM4G3AmcCHwf2AvZJsu8Yh90dOLGq9qqqm0bVszjJcJLhkZGRiTlJSZKkaWTGB0jgYODrVXVPVd1FNwBuD8yvqvObPicDz+4Zc/qoOb5ZVUU3aP6yqpY3l6WvARaOccybquqSsYqpqiVNOO0MDQ1t/llJkiRNU4MQIDfHmlGf72u+r+/Z3vB5rPtER4+XJEmaNQYhQP4QeHGSbZPsABxON+DdkeTgps8rgfM3NoEkSZLGb8Y/hV1VVyQ5HbgKuBW4vNn1KuAzSbYDfgq8uk8lSpIkDZR0b/3TZOh0OjU8PNzvMiRNkrV33TEh88zZcacJmUeStkSSpVXVGU/fQbiELUmSpClkgJQkSVIrBkhJkiS1MuMfopGkfvHeRUmzlSuQkiRJasUAKUmSpFYMkJIkSWrFeyAlaTP5HkhJs5UrkJIkSWrFAClJkqRWDJCSJElqxQApSZKkVgyQkiRJamWgA2SS1c33hUlW9LseSZKkQTDQAXJzJRnX643S5c9QkiTNKrMu/CSZk+QLSZYnWZbk0Kb92CRnJjkXOCfJ3CTnJLmi6fuipt/CJNcnOQVYAezWx9ORJEmacrPxReJvBKqq9kmyB3BWkic1+xYBT62q25tVyD+pqruS7AJckuTMpt/uwKuq6pLRkydZDCwGWLBgwaSfjCRJ0lSbdSuQwEHAFwGq6jrgJmBDgDy7qm5vtgN8MMnVwPeBxwC7NvtuGis8NnMuqapOVXWGhoYm6xwkSZL6ZjauQD6YNT3bRwNDwH5VdX+SG4E5Y/STJEmaVWbjCuQFdMMhzaXrBcD1Y/SbB9zahMdDgcdNXYmSJEnT12wMkCcCD0myHDgdOLaq7huj32lAp+l3DHDdFNYoSZI0baWq+l3DwOp0OjU8PNzvMiRNkrV33TEh88zZcacJmUeStkSSpVXVGU/f2bgCKUmSpC1ggJQkSVIrBkhJkiS14mt8JGkzee+ipNnKFUhJkiS1YoCUJElSKwZISZIktWKAlCRJUis+RCNJm8kXiUuarVyBlCRJUisGSEmSJLVigJQkSVIrBkhJkiS1YoCUJElSKwbIMSTZN8kf9Xz+4yTv7GdNkiRJ04UBcmz7Ar8KkFV1ZlV9eHSnJL4GSZIkzToDF4CSbA/8G/BYYCvgfcB/AR8D5gIrgWOr6udJzgMuBQ4F5gOvbT7/PbBtkoOADwHbAp2q+vMkJwFrgacDFwJ/OWUnJ0mSNA0MXIAE/hC4papeCJBkHvAd4EVVNZLkKOADwGua/ltX1QHNJev3VNVzkxxHExibOY4ddYzHAgdW1brRB0+yGFgMsGDBgok/O0mSpD4bxAC5HPhoko8A3wLuAPYGzk4C3VXJn/f0/1rzfSmwcJzHOGOs8AhQVUuAJQCdTqfaFi9JkjTdDVyArKofJ1lE9x7G9wPnAtdU1TM3MuS+5vs6xv/zWLNlVUqSJM1cA/cQTZJHA/dU1ReBE4BnAENJntns3ybJXpuY5m5gh8mtVJIkaWYauBVIYB/ghCTrgfuBNwAPAJ9s7ofcGvgEcM2DzPED4J1JrqT7EI0kSZIaqfI2vcnS6XRqeHi432VImiRr77pjQuaZs+NOEzKPJG2JJEurqjOevgN3CVuSJEmTywApSZKkVgyQkiRJamUQH6KRpCnhvYuSZitXICVJktSKAVKSJEmtGCAlSZLUigFSkiRJrfgQjSRtJl8kLmm2cgVSkiRJrRggJUmS1IoBUpIkSa0YICVJktSKAVKSJEmtGCAlSZLUSusAmeQhSXacjGKmiyTjer3RePtJkiQNknEFyCRfSrJjku2BFcB/JnnH5Jb2q2Mfk+TqJFclOTXJwiTnNm3nJFnQ9DspyaeTXJLkp0kOSfL5JNcmOalnvtVJPp7kmmb8UNN+XpJPJBkG3pzk8CSXJlmW5PtJdm36Hd/UcSFw6lT8DCRJkqaT8a5A7llVdwEvBr4DPB545aRV1UiyF/Bu4DlV9TTgzcA/AydX1VOB04BP9gzZCXgm8FbgTODjwF7APkn2bfpsDwxX1V7A+cB7esY/tKo6VfVR4EfA71bV04EvA3/V029P4LlV9X/GqHlxkuEkwyMjI1v4E5AkSZp+xhsgt0myDd0AeWZV3Q/U5JX1K88BzqiqlQBVdTvdgPilZv+pwEE9/b9ZVQUsB35ZVcuraj1wDbCw6bMeOL3Z/uKo8af3bD8W+F6S5cA76AbRDc6sqnvHKriqljQhtDM0NNTqZCVJkmaC8QbIzwI30l29+2GSxwF3TVZRW+C+5vv6nu0Nnzd2v2JvEF7Ts/3PwKeqah/gdcCcjfSTJEmaVcYVIKvqk1X1mKr6o+q6CTh0kmsDOBc4MsnOAEkeDlwEvKzZfzRwQcs5HwIc0Wy/nO6l6rHMA37WbL+q5TEkSZIG1oM+RZzkLzcx/mMTWMtvqaprknwAOD/JOmAZ8CbgC81DPCPAq1tOuwY4IMm7gVuBozbS73jgjCR30A2yj9+MU5AkSRo46d4yuJGdyYYHTJ4M7E/3wRSAw4HLquoVk1vexEuyuqrmTsWxOp1ODQ8PT8WhJPXB2rvumJB55uy404TMI0lbIsnSquqMp++DrkBW1XubCX8ILKqqu5vPxwPf3sI6JUmSNAON9yGaXYH/1/P5/zVtM85UrT5KkiQNqvH+JZVTgMuSfL35/GLgpEmpSJIkSdPauAJkVX0gyXeAg5umV1fVsskrS5KmP+9dlDRbbTJAJtkKuKaq9gCumPySJEmSNJ1t8h7IqloHXL/hb05LkiRpdhvvPZA7AdckuYyev8JSVX88KVVJkiRp2hpvgPy7Sa1CkqaZtatWTtmx5szfZcqOJUkTYbwP0ZyfZFe6LxOH7kvEb528siRJkjRdjes9kEleClwGHAm8FLg0yREPPkrr7FLvAAAgAElEQVSSJEmDaLyXsN8F7L9h1THJEPB94CuTVZgkSZKmp/H+JZqHjLpkfVuLsZIkSRog412B/G6S7wH/2nw+CviPySlJkiRJ09mDBsgkbwEuAv4WOBw4qNm1pKq+vtGBM0SS1wP3VNUpSY4FzqqqW/pcliRJ0rS2qRXIxwKfAPYAlgMX0g2UF01yXZMuydZV9ZmepmOBFcC4A2QzxwMTXZskSdJ09qABsqreDpDkoUAHOBB4NbAkyaqq2nPyS9w8Sf4OeAUwAtwMLAUOA66ku5L6r0l2AFYDN9I9v9OS3As8E3gH3VXXbekG5tdVVSU5r3cO4KNTd1aSJEn9N94HYbYFdgTmNV+3AJdOVlFbKsn+wEuApwEvoBsON3hoVXWq6lfBr6q+AgwDR1fVvlV1L/Cpqtq/qvame/6HPdgcPcdenGQ4yfDIyMgknJ0kSVJ/beoeyCXAXsDddAPjRcDHquqOKahtSzwL+EZVrQXWJvlmz77TxznHoUn+CtgOeDhwDbBhno3OUVVLgCUAnU6n2hYuSZI03W3qHsgFwMOAnwA/A/4XWDXZRU2yNZvqkGQOcCLQqaqbkxwPzGkzhyRJ0qB60EvYVfWHdP984T82TW8DLk9yVpL3TnZxW+BC4PAkc5LM5TcvP2/M3cAOzfaGsLiyGe9f3ZEkSWps8j2QVVXAiiSrgDubr8OAA4D3TG55m6eqLk9yJnA18Eu6T5DfuYlhJwGf6XmI5nN0n8r+BXD55FUrSZI0s6SbDzeyM/kLuk9eHwjcz69f4XMRsLyq1k9FkZsjydyqWp1kO+CHwOKqumIqa+h0OjU8PDyVh5Q0QdauWjllx5ozf5cpO5YkbUySpVXV2XTPTa9ALgTOAN5aVT/f0sKm2JIke9K9HH3yVIdHSZKkQbWp90D+5VQVMtGq6uX9rkGSJGkQjfc9kJIkSRIwjodoJGk28r5ESdo4VyAlSZLUigFSkiRJrRggJUmS1IoBUpIkSa34EI0kjcEXiUvSxrkCKUmSpFYMkJIkSWrFAClJkqRWDJCSJElqxQApSZKkVgyQkiRJamVaBsgkW23h+Cl5PdFUHUeSJGk6mfIAmWRhkuuSnJbk2iRfSbJdkhuTfCTJFcCRSfZNckmSq5N8PclOzfj9m7Yrk5yQZEXTfmySM5OcC5yTZG6Sc5JckWR5kheNOv5JSX7c1PHcJBcm+UmSA5p+ByS5OMmyJBclefJYx5nqn58kSVK/9WsF8snAiVX1FOAu4M+a9tuqalFVfRk4BfjrqnoqsBx4T9PnC8DrqmpfYN2oeRcBR1TV7wFrgT+pqkXAocBHk6Tp90Tgo8AezdfLgYOAtwN/2/S5Dji4qp4OHAd8cCPH+Q1JFicZTjI8MjLS+gcjSZI03fUrQN5cVRc221+kG94ATgdIMg+YX1XnN+0nA89OMh/Yoaoubtq/NGres6vq9mY7wAeTXA18H3gMsGuz74aqWl5V64FrgHOqqugG1YVNn3nAGc0K58eBvTZynN9QVUuqqlNVnaGhoXH9MCRJkmaSfgXI2sjnNVs4b+/4o4EhYL9mtfKXwJxm3309/db3fF7Pr/+84/uAH1TV3sDhPWMnok5JkqQZq18BckGSZzbbLwd+1Luzqu4E7khycNP0SuD8qloF3J3kGU37yx7kGPOAW6vq/iSHAo9rWeM84GfN9rEtx0qSJA2sfgXI64E3JrkW2An49Bh9XgWc0FyC3hf4+6b9tcDnklwJbA/cuZFjnAZ0kiwHjqF7T2Mb/wB8KMkyfr0qKUmSNOule+vfFB4wWQh8q7k0vDnj51bV6mb7ncCjqurNE1fhxOl0OjU8PNzvMiRthrWrVk7ZsebM32XKjiVJG5NkaVV1xtN3Jq6svTDJ39Ct/Sa8vCxJkjSlpjxAVtWNwGatPjbjT6d5WluSJElTb1r+JRpJkiRNXzPxErYkTTrvS5SkjXMFUpIkSa0YICVJktSKAVKSJEmtGCAlSZLUig/RSJp11t72836X8Bvm7PyofpcgSa24AilJkqRWDJCSJElqxQApSZKkVgyQkiRJasUAKUmSpFZm/VPYSbYCvgbsCPwP8NqqeqC/VUmSJE1fsz5AVtU64EWbMzbJ1oZNSZI02wzkJewk2yf5dpKrkqxIclSS30+yLMnyJJ9P8rCm741J3pvkimbfHk37AUkubsZclOTJTfuxSc5Mci5wTh9PU5IkqS8GMkACfwjcUlVPq6q9ge8CJwFHVdU+dFde39DTf2VVLQI+Dby9absOOLiqng4cB3ywp/8i4Iiq+r3RB06yOMlwkuGRkZGJPi9JkqS+G9QAuRx4XpKPJDkYWAjcUFU/bvafDDy7p//Xmu9Lm74A84AzkqwAPg7s1dP/7Kq6fawDV9WSqupUVWdoaGhCTkaSJGk6GcgA2QTFRXSD5PuBF29iyH3N93X8+r7Q9wE/aFYwDwfm9PRfM3HVSpIkzSwDGSCTPBq4p6q+CJwAPBNYmOSJTZdXAudvYpp5wM+a7WMno05JkqSZaFCfwt4HOCHJeuB+uvc7brgkvTVwOfCZTczxD8DJSd4NfHsyi5UkSZpJUlX9rmFgdTqdGh4e7ncZkkZZe9vP+13Cb5iz86P6XYIkkWRpVXXG03cgL2FLkiRp8hggJUmS1IoBUpIkSa0M6kM0krRR3nMoSVvGFUhJkiS1YoCUJElSKwZISZIkteI9kJJmHd8DKUlbxhVISZIktWKAlCRJUisGSEmSJLVigJQkSVIrBkhJkiS1YoDskWRhkhX9rkOSJGk6M0BugSS+BkmSJM06MzYAJfkwcHNV/Uvz+XhgNRDgpcDDgK9X1XuSLAS+A/wIOBD4GfCiqro3yX7A55tpz+qZfyvgw8AhzVz/UlWfTXII8D7gDmAP4EmTeZ6SJEnTzUxegTydblDc4KXACLA7cACwL7Bfkmc3+3enGwL3AlYBL2navwC8qaqeNmr+1wJ3VtX+wP7AnyZ5fLNvEfDmqvqt8JhkcZLhJMMjIyNbfJKSJEnTzYwNkFW1DHhEkkcneRrdFcF9gOcDy4Ar6K4Q7t4MuaGqrmy2lwILk8wH5lfVD5v2U3sO8XzgmCRXApcCO/fMdVlV3bCRupZUVaeqOkNDQxNyrpIkSdPJjL2E3TgDOAJ4JN0VyccBH6qqz/Z2ai5h39fTtA7YdhNzh+7K5PdGzXUIsGZLipYkSZrJZuwKZON04GV0Q+QZwPeA1ySZC5DkMUkesbHBVbUKWJXkoKbp6J7d3wPekGSbZq4nJdl+Es5BkiRpRpnRK5BVdU2SHYCfVdXPgZ8neQpwcRLoPlTzCrorjhvzauDzSYqeh2iA/w9YCFyR7mQjwIsn/iwkSZJmllRVv2sYWJ1Op4aHh/tdhqRR1t72836X8Bvm7PyofpcgSSRZWlWd8fSd6ZewJUmSNMUMkJIkSWrFAClJkqRWZvRDNJK0ObznUJK2jCuQkiRJasUAKUmSpFYMkJIkSWrFAClJkqRWfIhG0oyy9hc39LuECTfnkY/vdwmS1IorkJIkSWrFAClJkqRWDJCSJElqxQApSZKkVgyQkiRJamXaB8gkF03hsQ5JcuBUHU+SJGkmmvYBsqqmMtAdArQ6XhJfhSRJkmaVaR8gk6xuvh+S5LwkX0lyXZLT0vWHSc7o6X9Ikm81289PcnGSK5KckWRu035jkvc27cuT7JFkIfB64K1JrkxycJLDk1yaZFmS7yfZtRl/fJJTk1wInDrFPxJJkqS+mvYBcpSnA28B9gR+B3gW8H3gGUm2b/ocBXw5yS7Au4HnVtUiYBj4y565VjbtnwbeXlU3Ap8BPl5V+1bVBcCPgN+tqqcDXwb+qmf8ns3c/6e3wCSLkwwnGR4ZGZnIc5ckSZoWZlqAvKyq/req1gNXAgur6gHgu8DhzeXkFwLfAH6Xbsi7MMmVwKuAx/XM9bXm+1Jg4UaO91jge0mWA+8A9urZd2ZV3Tt6QFUtqapOVXWGhoY29zwlSZKmrZl2/959Pdvr+HX9Xwb+HLgdGK6qu5MEOHv0CuEYc/XOM9o/Ax+rqjOTHAIc37NvTfvyJUmSZr6ZtgK5MecDi4A/pRsmAS4BnpXkiQBJtk/ypE3MczewQ8/necDPmu1XTVy5kiRJM9dABMiqWgd8C3hB852qGgGOBf41ydXAxcAem5jqm8CfbHiIhu6K4xlJlgIrJ6d6SZKkmSVV1e8aBlan06nh4eF+lyENlLW/uKHfJUy4OY98fL9LkCSSLK2qznj6DsQKpCRJkqaOAVKSJEmtGCAlSZLUykx7jY+kWc77BSWp/1yBlCRJUisGSEmSJLVigJQkSVIrBkhJkiS14kM0kmYUXyQuSf3nCqQkSZJaMUBKkiSpFQOkJEmSWjFASpIkqRUDpCRJklqZlgEyyfFJ3t6H474lyXZTfVxJkqSZZFoGyH5IshXwFqBVgGzGSZIkzRrTJkAmeVeSHyf5EfDkpu0JSb6bZGmSC5Ls0bQfmWRFkquS/LBpOzbJN5Kcl+QnSd7TM/crklyW5Mokn90Q+pKsTvLRJFcB7wIeDfwgyQ+a/Z9OMpzkmiTv7ZnvxiQfSXIFcOQU/YgkSZKmhWnxIvEk+wEvA/alW9MVwFJgCfD6qvpJkmcAJwLPAY4D/qCqfpZkfs9UBwB7A/cAlyf5NrAGOAp4VlXdn+RE4GjgFGB74NKqeltTx2uAQ6tqZTPfu6rq9iZwnpPkqVV1dbPvtqpaNMa5LAYWAyxYsGBCfj6SJEnTybQIkMDBwNer6h6AJGcCc4ADgTOSbOj3sOb7hcBJSf4N+FrPPGdX1W3NHF8DDgIeAPajGygBtgVubfqvA776IHW9tAmEWwOPAvYENgTI08caUFVL6AZfOp1OberEJUmSZprpEiDH8hBgVVXtO3pHVb2+WZF8IbC0WcEEGB3YCghwclX9zRjHWFtV68Y6eJLHA28H9q+qO5KcRDfUbrCm1dlIkiQNiOlyD+QPgRcn2TbJDsDhdC9D35DkSIB0Pa3ZfkJVXVpVxwEjwG7NPM9L8vAk2wIvprtSeQ5wRJJHNGMfnuRxG6njbmCHZntHuiHxziS7Ai+Y4HOWJEmakabFCmRVXZHkdOAqupeXL292HQ18Osm7gW2ALzd9TkiyO93VxXOatn2By+hekn4s8MWqGgZoxp+V5CHA/cAbgZvGKGUJ8N0kt1TVoUmWAdcBN9MNo5IkSbNeqgbjNr0kxwKdqvrzfteyQafTqeHh4X6XIQ2Utb+4od8lTLg5j3x8v0uQJJIsrarOePpOl0vYkiRJmiGmxSXsiVBVJwEn9bkMSZKkgecKpCRJkloZmBVISbOD9wtKUv+5AilJkqRWDJCSJElqxQApSZKkVrwHUtK0ce8t/9XvEvpi20c/sd8lSFIrrkBKkiSpFQOkJEmSWjFASpIkqRUDpCRJkloxQEqSJKmVgQiQSRYmWdGi/0lJjhij/ZAk35rY6iRJkgbLQATIfknia5AkSdKsM0gBcqskn0tyTZKzkmyb5E+TXJ7kqiRfTbJdT//nJhlO8uMkh42eLMkBSS5OsizJRUme3LQfm+TMJOcC50zVyUmSJE0XgxQgdwf+par2AlYBLwG+VlX7V9XTgGuB1/b0XwgcALwQ+EySOaPmuw44uKqeDhwHfLBn3yLgiKr6vdFFJFncBNPhkZGRCTo1SZKk6WOQLsHeUFVXNttL6QbEvZO8H5gPzAW+19P/36pqPfCTJD8F9hg13zzg5CS7AwVs07Pv7Kq6fawiqmoJsASg0+nUlp2SJEnS9DNIK5D39WyvoxuOTwL+vKr2Ad4L9K4yjg53oz+/D/hBVe0NHD5q7JqJKFiSJGkmGqQAOZYdgJ8n2QY4etS+I5M8JMkTgN8Brh+1fx7ws2b72EmtUpIkaQYZ9AD5d8ClwIV072ns9T/AZcB3gNdX1dpR+/8B+FCSZQzWpX5JkqQtkipv05ssnU6nhoeH+12GNGPce8t/9buEvtj20U/sdwmSRJKlVdUZT99BX4GUJEnSBDNASpIkqRUDpCRJklrx4RBJ04b3AkrSzOAKpCRJkloxQEqSJKkVA6QkSZJaMUBKkiSpFR+ikTQl7r352n6XMG1tu9tT+l2CJLXiCqQkSZJaMUBKkiSpFQOkJEmSWjFASpIkqRUDpCRJkloxQPZIckiSA/tdhyRJ0nQ2IwJkkq2m6FCHAK0CZBJfhSRJkmaVvgfIJAuTXJfktCTXJvlKku2S3JjkI0muAI5M8oQk302yNMkFSfZoxj8hySVJlid5f5LVTfshSb7Vc5xPJTm22b4xyXuTXNGM2yPJQuD1wFuTXJnk4CSHJ7k0ybIk30+yazP++CSnJrkQOHVKf2CSJEl91vcA2XgycGJVPQW4C/izpv22qlpU/397dx9kWV3fefz9cSDylPCgExdXYCROgoAwQINiIZIKMWqqRAqNggqI5eiGh2KV3VBhazUaq0STuGpEGF3jEwjhSYnJgkIYCqgMOAPDwAADChhMWB2UZxBl+O4f99fLpdPD3DN09+3b835V3brn/M7vnPP9fadpvv07595TdS6wBDixqvYDTgHOaH0+C3y2ql4F/KTDOe+vqn2BLwKnVNU9wJnAZ6pqUVVdDVwDvKaq9gHOBf573/67A4dW1ZH9B02yOMnyJMvXrl3bIRxJkqTRMFsKyHur6tq2/E3goLZ8HkCSbehdWj4/yUrgLGDH1udA4Py2fE6Hc17U3lcAC9bT52XAZUluBv4bsEfftkuq6omJO1TVkqoaq6qx+fPndwhHkiRpNMyW+/dqPeuPtfcXAA9W1aIOx3yKZxfIW0zY/mR7X8f68/B54G+q6pIkhwAf7dv22KR7SJIkzXGzZQZy5yQHtuWj6F06/v+q6mHg7iRvB0jP3m3zMuCItvzOvt1+DOye5IVJtgP+YIA4HgF+s299W+Df2vIxgw5GkiRpLpstBeQa4PgktwHb07svcaJ3Ae9LchOwGjistZ8MfCjJKuAVwEMAVXUv8PfALe39xgHi+Afg8PEP0dCbcTw/yQrg/o0cmyRJ0pySqolXj2c4gN6nn79bVXtu5P5bAU9UVSV5J3BkVR22of1mwtjYWC1fvnzYYUizwhP33jbsEGatLXd65bBDkCSSrKiqsUH6zpZ7IJ+P/YC/TRLgQeC4IccjSZI0pw29gGxfn7NRs49t/6uBvTfYUZIkSVNittwDKUmSpBEx9BlISZsG7/OTpLnDGUhJkiR1YgEpSZKkTiwgJUmS1In3QEp63p64Z9WwQxhpWy7Ya9ghSFInzkBKkiSpEwtISZIkdWIBKUmSpE4sICVJktSJBaQkSZI6sYCUJElSJxaQkiRJ6sQC8nlKMm/YMUiSJM2kkS4gkyxIcnuSs5PcluSCJFsl2S/JVUlWJLksyY6t/9Ikpye5PskdSV7X2vdobSuTrEqysLV/ux1jdZLFfed9NMlfJ7kJOHAog5ckSRqSkS4gm98DzqiqVwIPA8cDnwfeVlX7AV8BPtHXf7OqOgA4GfhIa/sg8NmqWgSMAT9p7ce1Y4wBJyV5UWvfGriuqvauqmv6g0myOMnyJMvXrl075YOVJEkatrnwKMN7q+ratvxN4M+BPYHvJwGYB9zX1/+i9r4CWNCW/wU4LcnLgIuq6s7WflKSw9vyTsBC4OfAOuDCyYKpqiXAEoCxsbF6XiOTJEmaheZCATmxSHsEWF1V67u0/GR7X0cbf1Wdk+Q64I+Bf0ryAeBp4FDgwKp6PMlSYIu27y+rat0UjkGSJGlkzIVL2DsnGS8WjwKWAfPH25JsnmSP5zpAkl2Bu6rqc8B3gL2AbYEHWvG4G/CaaRuBJEnSCJkLBeQa4PgktwHb0+5/BE5vH3JZCbx2A8f4E+CWJCvpXf7+OnApsFk77ifpFaaSJEmbvLlwCfupqnr3hLaVwMETO1bVIX3L99PugayqT9IrEid602QnrKptNjJWSZKkkTcXZiAlSZI0g0Z6BrKq7qF3yVmSJEkzxBlISZIkdTLSM5CSZoctF+w17BAkSTPIGUhJkiR1YgEpSZKkTiwgJUmS1In3QEqbsMdu9/vxZ4Otd/NBV5JGizOQkiRJ6sQCUpIkSZ1YQEqSJKkTC0hJkiR1YgEpSZKkTiwg+yT582HHIEmSNNsNtYBMMm+Y559E5wJyFo5BkiRpWk1rAZnk20lWJFmdZHFrezTJXye5CTgwyZuT3N76fS7Jd1u/jyY5pe9YtyRZ0F63J/lqkjuSnJ3k0CTXJrkzyQGt/9ZJvpLk+iQ3JjmstR+b5KIkl7b+n2rtnwS2TLIyydnri3+yMUxnDiVJkmab6f4i8eOq6hdJtgR+kORCYGvguqr6cJItgDuBg6vq7iTfGvC4rwDeDhwH/AA4CjgIeAu9WcS3AqcB/1xVxyXZDrg+yeVt/0XAPsCTwJokn6+qU5OcUFWLniv+qvp5/xgmBtYKzcUAO++886B5kiRJGhnTfQn7pDZLtwzYCVgIrAMubNt3A+6qqrvb+qAF5N1VdXNVPQ2sBq6oqgJuBha0Pm8ATk2yElgKbAGMV3RXVNVDVfVL4FZglw7xM2EMz1JVS6pqrKrG5s+fP+BwJEmSRse0zUAmOQQ4FDiwqh5PspReEffLqlo3wCGe4tkF7hZ9y0/2LT/dt/40z4wpwBFVtWZCXK+esP86JsnDc8RPhzFIkiTNOdM5A7kt8EArvnYDJnvY6xpg1yQL2vo7+rbdA+wLkGRf4OUdz38ZcGKStGPsM8A+v06yeYf4JUmSNjnTWUBeCmyW5Dbgk/QuAz9LVT0B/ClwaZIVwCPAQ23zhcAOSVYDJwB3dDz/x4HNgVXtGB8fYJ8lrf/Zg8QvSZK0KUrv1sEhBpBsU1WPtpnCLwB3VtVnhhrUFBkbG6vly5cPOwxpvR673b+LZoOtd/MCh6ThS7KiqsYG6Tsbvkj8/e2DLqvpXTY+a8jxSJIk6TlM99f4bFCbbZwTM46SJEmbgtkwAylJkqQRMvQZSEnD4713kqSN4QykJEmSOrGAlCRJUicWkJIkSerEAlKSJEmd+CEaaYY9cM3Fww5Bs8z2Bx0+7BAkqRNnICVJktSJBaQkSZI6sYCUJElSJxaQkiRJ6sQCUpIkSZ1YQEqSJKmTkS0g0zPU+JP4NUiSJGmTM1IFZJIFSdYk+TpwC/C/kyxPsjrJX7Q+Y0lWttfNSaq1L0qyLMmqJBcn2b61L01yepLrk9yR5HV957o6yQ3t9drWfkhrvwS4dSiJkCRJGqJRnEFbCBxTVcuS7FBVv0gyD7giyV5VtRxYBJDk08Clbb+vAydW1VVJPgZ8BDi5bdusqg5I8ubWfijwM+APq+qXSRYC3wLGWv99gT2r6u6JwSVZDCwG2Hnnnad+9JIkSUM2UjOQzY+rallb/pMkNwA3AnsAu493SvIOeoXeqUm2Bbarqqva5q8BB/cd86L2vgJY0JY3B76U5Gbg/P5jA9dPVjwCVNWSqhqrqrH58+dv7BglSZJmrVGcgXwMIMnLgVOA/avqgSRfBbZo2/YEPgocXFXrkmzomE+293U8k5P/CvwU2Jteof3LiTFIkiRtikZxBnLcb9Er5B5K8hLgTQBJtqN3ufnoqloLUFUPAQ+M398IvAe46j8e8lm2Be6rqqdb/3lTPwRJkqTRM4ozkABU1U1JbgRuB+4Frm2bDgN2oXf5ebzvIuAY4MwkWwF3Ae/dwCnOAC5McjS9+yiddZQkSQJSVcOOYc4aGxur5cuXDzsMzTIPXHPxsEPQLLP9QYcPOwRJIsmKqhrbcM/RvoQtSZKkIbCAlCRJUicWkJIkSepkZD9EI40q73eTJI06ZyAlSZLUiQWkJEmSOrGAlCRJUifeAzlH3Pg/Txx2CJI20j4f+/ywQ5CkTpyBlCRJUicWkJIkSerEAlKSJEmdWEBKkiSpEwtISZIkdTJSBWSSBUlume3HlCRJmstGqoAcpiTzhh2DJEnSbDCKBeS8JF9KsjrJ95JsmWRRkmVJViW5OMn2AEmWJvlMkuVJbkuyf5KLktyZ5C/7jrlZkrNbnwuSbNX2vyfJ6UluAN6e5HeSXJpkRZKrk+w2jARIkiQN0ygWkAuBL1TVHsCDwBHA14E/q6q9gJuBj/T1/1VVjQFnAt8Bjgf2BI5N8qLW5/eAM6rqlcDDwJ/27f/zqtq3qs4FlgAnVtV+wCnAGRODS7K4FazL165dO3WjliRJmiVGsYC8u6pWtuUVwO8A21XVVa3ta8DBff0vae83A6ur6r6qehK4C9ipbbu3qq5ty98EDurb/zyAJNsArwXOT7ISOAvYcWJwVbWkqsaqamz+/PnPZ5ySJEmz0ig+yvDJvuV1wHYD9n96wr5P88z4a8I+/euPtfcXAA9W1aLBQ5UkSZp7RnEGcqKHgAeSvK6tvwe46jn6T2bnJAe25aOAayZ2qKqHgbuTvB0gPXtvZMySJEkjay4UkADHAJ9OsgpYBHys4/5rgOOT3AZsD3xxPf3eBbwvyU3AauCwjYxXkiRpZI3UJeyquofeB2DG1/+qb/NrJul/SN/yUmDpZNuAST9NXVULJqzfDbyxQ8iSJElzzlyZgZQkSdIMsYCUJElSJxaQkiRJ6sQCUpIkSZ2M1IdotH77fOzzww5BkiRtIpyBlCRJUiepmvgQFk2VJI/Q+45JTZ8XA/cPO4g5zhxPP3M8/czx9DK/028mcrxLVQ30HGYvYU+vNVU1Nuwg5rIky83x9DLH088cTz9zPL3M7/SbbTn2ErYkSZI6sYCUJElSJxaQ02vJsAPYBJjj6WeOp585nn7meHqZ3+k3q3Lsh2gkSZLUiTOQkiRJ6sQCUpIkSZ1YQE6hJDsk+X6SO9v79pP02SXJDUlWJlmd5IPDiHVUDZjjRUn+peV3VZJ3DCPWUTVIjlu/S5M8mOS7Mx3jqEryxiRrkvwwyamTbH9hkvPa9uuSLJj5KEfXAP/cuvkAAAd1SURBVPk9uP3+fSrJ24YR46gbIMcfSnJr+917RZJdhhHnKBsgxx9McnOrI65Jsvsw4rSAnFqnAldU1ULgirY+0X3AgVW1CHg1cGqSl85gjKNukBw/DhxdVXsAbwT+V5LtZjDGUTdIjgE+DbxnxqIacUnmAV8A3gTsDhw5yS/+9wEPVNUrgM8Ap89slKNrwPz+K3AscM7MRjc3DJjjG4GxqtoLuAD41MxGOdoGzPE5VfWqVkd8CvibGQ4TsICcaocBX2vLXwPeOrFDVf2qqp5sqy/Ef4OuBsnxHVV1Z1v+d+BnwEDfrC9ggBwDVNUVwCMzFdQccADww6q6q6p+BZxLL9f9+nN/AfAHSTKDMY6yDea3qu6pqlXA08MIcA4YJMdXVtXjbXUZ8LIZjnHUDZLjh/tWtwaG8mloi5ep9ZKquq8t/1/gJZN1SrJTklXAvcDprcjRYAbK8bgkBwC/AfxougObQzrlWAP7z/T+mx/3k9Y2aZ+qegp4CHjRjEQ3+gbJr56frjl+H/B/pjWiuWegHCc5PsmP6M1AnjRDsT2LjzLsKMnlwH+aZNNp/StVVUkm/augqu4F9mqXrr+d5IKq+unURzuapiLH7Tg7At8AjqkqZxz6TFWOJWkySd4NjAGvH3Ysc1FVfQH4QpKjgP8BHDPTMVhAdlRVh65vW5KfJtmxqu5rxcvPNnCsf09yC/A6eperxNTkOMlvAf8InFZVy6Yp1JE1lT/HGti/ATv1rb+stU3W5ydJNgO2BX4+M+GNvEHyq+dnoBwnOZTeH6Ov77tlS4Pp+nN8LvDFaY1oPbyEPbUu4Zm/Ao4BvjOxQ5KXJdmyLW8PHASsmbEIR98gOf4N4GLg61VlYd7dBnOsjfIDYGGSl7ef0XfSy3W//ty/Dfjn8mkPgxokv3p+NpjjJPsAZwFvqSr/+OxukBwv7Fv9Y+DOGYzvGVXla4pe9O5VuqL9Y14O7NDax4Avt+U/BFYBN7X3xcOOe5ReA+b43cCvgZV9r0XDjn1UXoPkuK1fDawFnqB3n84fDTv22f4C3gzcQe+e3NNa28fo/c8WYAvgfOCHwPXArsOOeZReA+R3//az+hi9md3Vw4551F4D5Phy4Kd9v3svGXbMo/YaIMefBVa3/F4J7DGMOH2UoSRJkjrxErYkSZI6sYCUJElSJxaQkiRJ6sQCUpIkSZ1YQEqSJKkTC0hJm5wk65KsTLI6yU1JPpzkBW3bWJLPTdF5jm1PnBpf/3KS3afouGvbGG5N8v6O+2/0GJOcnGSrvvV/SrLdxhxL0ujya3wkbXKSPFpV27Tl3wbOAa6tqo88xz6bVe/51F3OsxQ4paqWP594JznuscBYVZ3Q4l8N7Fl9j0TdmHgHPPc97dz3T/WxJY0OZyAlbdKq97SMxcAJ6TkkyXcBknw0yTeSXAt8I8m8JJ9O8oMkq5J8YPw4Sf4syc1tRvOTSd5G78vXz24zhVsmWZpkrPU/svW/Jcnpfcd5NMkn2nGWJXnJAPH/CNhlkni3SPJ37Tw3Jvn9do7+MW6d5CtJrm99Dmvt85L8VYtvVZITk5wEvBS4MsmVrd89SV7clj/U+t+S5OTWtiDJbUm+1GZ8vzf+NC5Jo8tnYUva5FXVXUnmAb89yebdgYOq6okki4GHqmr/JC8Erk3yPWA34DDg1VX1eJIdquoXSU6gbwYyCe39pcDpwH7AA8D3kry1qr4NbA0sq6rTknwKeD/wl+uLPcmuwK70nl4zMd4P94ZXr0qyWzvP7044xGn0Hpl4XLsUfX2Sy4GjgQX0nuL0VN+YPgT8/sQZyCT7Ae8FXg0EuC7JVW18C4Ejq+r9Sf4eOAL45vrGJGn2cwZSkp7bJVX1RFt+A3B0kpXAdfQe+7gQOBT4u6p6HKCqfrGBY+4PLK2qte0y89nAwW3br4DvtuUV9Iq4ybyjxfEt4AN95+yP9yBaoVZVtwM/BiYWkG8ATm3HWkrvcYo7tzGdNX4ZfIAxHQRcXFWPVdWjwEXA69q2u6tq5QBjkjQinIGUtMlrs3jrgJ8Br5yw+bH+rsCJVXXZhP3/aArD+XU9c3P6Otb/e/q8qjphkvbHJml7LgGOqKo1z2pss6VT5Mm+5XWAl7ClEecMpKRNWpL5wJnA3/YVbutzGfBfkmze9v3dJFsD3wfeO/7p5CQ7tP6PAL85yXGuB16f5MXt0vmRwFXPfzT/wdXAu8ZjpTezuGZCn8uAE9MqxiT7tPbvAx9Isllr39CYrgbemmSrlpPDW5ukOcgCUtKmaMv2wZbVwOXA94C/GGC/LwO3AjckuQU4C9isqi4FLgGWt0vBp7T+XwXOHP8QzfhBquo+4FTgSuAmYEVVfWdqhvYsZwAvSHIzcB5wbFWNzwaOF8sfBzYHVrV8fLxvrP/a2m8CjmrtS4BLxz9E0zemG+iN93p6l/e/XFU3TsOYJM0Cfo2PJG1ikhwBvKWqjhl2LJJGk/dAStImJMlbgE8Axw07FkmjyxlISZIkdeI9kJIkSerEAlKSJEmdWEBKkiSpEwtISZIkdWIBKUmSpE7+H1DVm+XQ3zcXAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E4mz_klb_4n6"
      },
      "source": [
        "### Colectivos\n",
        "\n",
        "Las palabras referidas a colectivos se han usado muchas veces en lenguaje igualitario, porque, aunque tienen género morfológico, ese género no se corresponde con el género de las personas que componen el colectivo.\n",
        "\n",
        "Podríamos usar las palabras de colectivos profesionales también como indicadores de género neutro!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wxVgzTdt_8n8"
      },
      "source": [
        "profesiones_colectivos = [\n",
        "'ingeniería',\n",
        "'arquitectura',\n",
        "'psicología',\n",
        "'enfermería',\n",
        "'medicina',\n",
        "'carpintería',\n",
        "'presidencia',\n",
        "'biología',\n",
        "'cocina',\n",
        "'docencia',\n",
        "'abogacía',\n",
        "'cirugía',\n",
        "'neurocirugía',\n",
        "'actuación',\n",
        "'música',\n",
        "'canto'\n",
        "]"
      ],
      "execution_count": 152,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 621
        },
        "id": "6RmGNCuG_51M",
        "outputId": "22b6da7c-517a-48b9-91ba-559bf4193a1f"
      },
      "source": [
        "f, ax = plt.subplots(1, figsize=(10, 10))\n",
        "\n",
        "we_genero.plot_projection_scores(\n",
        "    profesiones_colectivos + ['hombre'] + ['mujer'],\n",
        "    n_extreme=20,\n",
        "    ax=ax);"
      ],
      "execution_count": 153,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rG5eTraUIuX5"
      },
      "source": [
        "### Sustantivos abstractos"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A0OP1H9kAUOU"
      },
      "source": [
        "sustantivos_abstractos = [\n",
        "'inteligencia',\n",
        "'belleza',\n",
        "'humildad',\n",
        "'sabiduría',\n",
        "'poder',\n",
        "'cariño',\n",
        "'bondad',\n",
        "'ambición',\n",
        "'delicadeza',\n",
        "'amabilidad',\n",
        "'paciencia',\n",
        "'popularidad',\n",
        "'fama',\n",
        "'generosidad',\n",
        "'honestidad',\n",
        "'canto',\n",
        "'maldad',\n",
        "'soberbia',\n",
        "'violencia'\n",
        "]\n"
      ],
      "execution_count": 141,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 621
        },
        "id": "ptbtWCx1AQ1S",
        "outputId": "a368bed4-7acc-40e7-f6f0-5eb0517f7a9c"
      },
      "source": [
        "f, ax = plt.subplots(1, figsize=(10, 10))\n",
        "\n",
        "we_genero.plot_projection_scores(\n",
        "    sustantivos_abstractos + ['hombre'] + ['mujer'],\n",
        "    n_extreme=20,\n",
        "    ax=ax);"
      ],
      "execution_count": 142,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "03yUvAuyACiv"
      },
      "source": [
        "### Adjetivos neutros"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m29pPsvmAEUz"
      },
      "source": [
        "adjetivos_neutros = [\n",
        "'inteligente',\n",
        "'humilde',\n",
        "'amable',\n",
        "'dulce',\n",
        "'audaz',\n",
        "'paciente',\n",
        "'popular',\n",
        "'flexible',\n",
        "'grande',\n",
        "'brillante',\n",
        "'inocente',\n",
        "'fácil',\n",
        "'agradable',\n",
        "'infeliz',\n",
        "'capaz',\n",
        "'difícil',\n",
        "'temperamental',\n",
        "]"
      ],
      "execution_count": 139,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 621
        },
        "id": "YnYd6L1K_7eS",
        "outputId": "aff46f0f-51cd-4969-e9c0-d8a51fc0fb01"
      },
      "source": [
        "f, ax = plt.subplots(1, figsize=(10, 10))\n",
        "\n",
        "we_genero.plot_projection_scores(\n",
        "    adjetivos_neutros + ['hombre'] + ['mujer'],\n",
        "    n_extreme=20,\n",
        "    ax=ax);"
      ],
      "execution_count": 140,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2-CTEVbS_S2n"
      },
      "source": [
        "## 4.6 ¿Podemos incorporar palabras con género morfológico marcado?\n",
        "\n",
        "Incluso usando todas estas palabras con género semántico neutro, habrá muchas palabras que quedarán sesgadas, porque no se habrán podido neutralizar.\n",
        "\n",
        "¿Habrá alguna forma de incorporar las palabras con género marcado morfológicamente para hacer la neutralización?\n",
        "\n",
        "Veamos algunas de sus propiedades"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GJk195Hqi2Ae"
      },
      "source": [
        "palabras_con_genero = ['gato','gata']"
      ],
      "execution_count": 267,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "id": "AQp2rGrEi_V1",
        "outputId": "ede718e3-e9ce-4ee9-d554-63beff9e0b62"
      },
      "source": [
        "f, ax = plt.subplots(1, figsize=(10, 2))\n",
        "\n",
        "we_genero.plot_projection_scores(\n",
        "    palabras_con_genero + espacio_f[:1] + espacio_m[:1],\n",
        "    n_extreme=20,\n",
        "    ax=ax);"
      ],
      "execution_count": 270,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x144 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pGi2NHg9_UYw"
      },
      "source": [
        "profesiones_con_genero = [\n",
        "                          'arquitecta', 'arquitecto',\n",
        "                          'ingeniera', 'ingeniero',\n",
        "                          'diseñadora', 'diseñador',\n",
        "                          'doctor', 'doctora',\n",
        "                          'abogada', 'abogado',\n",
        "                          'profesor', 'profesora',\n",
        "                          'contador','contadora',\n",
        "                          'científico', 'científica',\n",
        "                          'biólogo', 'bióloga',\n",
        "                          'cocinera', 'cocinero',\n",
        "                          'psicóloga','psicólogo',\n",
        "                          'enfermera', 'enfermero',\n",
        "                          'obrera', 'obrero',\n",
        "                          'actor', 'actriz'\n",
        "                          ]"
      ],
      "execution_count": 114,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 621
        },
        "id": "Gm95_kS6_XKo",
        "outputId": "8661a221-8276-4dc3-e675-56b08ccb064b"
      },
      "source": [
        "f, ax = plt.subplots(1, figsize=(10, 10))\n",
        "\n",
        "we_genero.plot_projection_scores(\n",
        "    profesiones_con_genero + espacio_f[:1] + espacio_m[:1],\n",
        "    n_extreme=20,\n",
        "    ax=ax);"
      ],
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w269IRa3Ah-4"
      },
      "source": [
        "Las profesiones con género morfológico femenino están más hacia el lado femenino que las profesiones masculinas hacia el lado masculino.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QRqNKHfw_ddS"
      },
      "source": [
        "Podemos buscar otra forma de calcular el sesgo cuando la mayoría de las palabras tienen una forma femenina y otra masculina: podemos medir el sesgo como el valor absoluto de la diferencia entre la proyección de cada palabra. Cuanto más grande sea esa diferencia, más estereotipada hacia uno de los dos géneros está esa profesión."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JFGn9qyg_oeV"
      },
      "source": [
        "gender_direction = we_genero.direction"
      ],
      "execution_count": 130,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wF0wkvExjRcg",
        "outputId": "873340cc-31cd-43ac-c44b-478ff342ac10"
      },
      "source": [
        "abs(gender_direction @ we.word_vec('gato')) - abs(gender_direction @ we.word_vec('gata'))"
      ],
      "execution_count": 262,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0015478581"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 262
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "axaQfjdH_hyR",
        "outputId": "edd2492b-2e7c-4c99-e3d0-7a4f765855d3"
      },
      "source": [
        "abs(gender_direction @ we.word_vec('arquitecta')) - abs(gender_direction @ we.word_vec('arquitecto'))"
      ],
      "execution_count": 131,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.098457724"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 131
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2ul9nN0OA2np",
        "outputId": "693f41b0-0921-4ba9-cc51-63629b0e4727"
      },
      "source": [
        "abs(gender_direction @ we.word_vec('médica')) - abs(gender_direction @ we.word_vec('médico'))"
      ],
      "execution_count": 133,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-0.024638467"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 133
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VWWUySLY_v7R",
        "outputId": "dd07b43b-46cb-44e8-8cd7-6fc3c069bd23"
      },
      "source": [
        "abs(gender_direction @ we.word_vec('enfermera')) - abs(gender_direction @ we.word_vec('enfermero'))"
      ],
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.21549931"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 118
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dqSvbe2sC3vd"
      },
      "source": [
        "# Resources\n",
        "\n",
        "## [Doing Data Science Responsibly - Resources](https://handbook.responsibly.ai/appendices/resources.html)\n",
        "\n",
        "In particular:\n",
        "\n",
        "- Timnit Gebru and Emily Denton - CVPR 2020 - [FATE Tutorial](https://youtu.be/-xGvcDzvi7Q) [Video]\n",
        "\n",
        "- Rachel Thomas - fast.ai - [Algorithmic Bias (NLP video 16)](https://youtu.be/pThqge9QDn8) [Video]\n",
        "\n",
        "- Solon Barocas, Moritz Hardt, Arvind Narayanan - [Fairness and machine learning - Limitations and Opportunities](https://fairmlbook.org/) [Textbook]\n",
        "\n",
        "## [Course // Responsible AI, Law, Ethics & Society](https://learn.responsibly.ai/)\n",
        "\n",
        "## [Course // Ethic aspects in AI (in spanish, from UBA)](https://campus.exactas.uba.ar/course/view.php?id=1477&section=0)\n",
        "\n",
        "\n",
        "## Non-Technical Overview with More Downstream Application Examples\n",
        "- [Google - Text Embedding Models Contain Bias. Here's Why That Matters.](https://developers.googleblog.com/2018/04/text-embedding-models-contain-bias.html)\n",
        "- [Kai-Wei Chang (UCLA) - What It Takes to Control Societal Bias in Natural Language Processing](https://www.youtube.com/watch?v=RgcXD_1Cu18)\n",
        "- Sun, T., Gaut, A., Tang, S., Huang, Y., ElSherief, M., Zhao, J., ... & Wang, W. Y. (2019). [Mitigating Gender Bias in Natural Language Processing: Literature Review](https://arxiv.org/pdf/1906.08976.pdf). arXiv preprint arXiv:1906.08976."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6jQnG0Jq_ccD"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4LtaZOPmC3vd"
      },
      "source": [
        "## Critical Prespective on Bias in NLP\n",
        "\n",
        "Blodgett, S. L., Barocas, S., Daumé III, H., & Wallach, H. (2020). [Language (Technology) is Power: A Critical Survey of \"Bias\" in NLP](https://arxiv.org/pdf/2005.14050.pdf). arXiv preprint arXiv:2005.14050.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BlguqiRoNaG9"
      },
      "source": [
        "## Additional Related Work\n",
        "\n",
        "- **Software Framework for Word Embedding Bias**\n",
        "  - [WEFE: The Word Embeddings Fairness Evaluation Framework](https://wefe.readthedocs.io/en/latest/)\n",
        "\n",
        "- **Understanding Bias**\n",
        "    - Ethayarajh, K., Duvenaud, D., & Hirst, G. (2019, July). [Understanding Undesirable Word Embedding Associations](https://arxiv.org/pdf/1908.06361.pdf). In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (pp. 1696-1705). - **Including critical analysis of the current metrics and debiasing methods (quite technical)**\n",
        "\n",
        "  - Brunet, M. E., Alkalay-Houlihan, C., Anderson, A., & Zemel, R. (2019, May). [Understanding the Origins of Bias in Word Embeddings](https://arxiv.org/pdf/1810.03611.pdf). In International Conference on Machine Learning (pp. 803-811).\n",
        "\n",
        "- **Discovering Bias**\n",
        "  - Swinger, N., De-Arteaga, M., Heffernan IV, N. T., Leiserson, M. D., & Kalai, A. T. (2019, January). [What are the biases in my word embedding?](https://arxiv.org/pdf/1812.08769.pdf). In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (pp. 305-311). ACM.\n",
        "    Measuring Gender Bias in Word Embeddings across Domains and Discovering New Gender Bias Word Categories\n",
        "  \n",
        "  - Chaloner, K., & Maldonado, A. (2019, August). [Measuring Gender Bias in Word Embeddings across Domains and Discovering New Gender Bias Word Categories](https://www.aclweb.org/anthology/W19-3804). In Proceedings of the First Workshop on Gender Bias in Natural Language Processing (pp. 25-32).\n",
        "\n",
        "- **Mitigating Bias**\n",
        "  - Maudslay, R. H., Gonen, H., Cotterell, R., & Teufel, S. (2019). [It's All in the Name: Mitigating Gender Bias with Name-Based Counterfactual Data Substitution](https://arxiv.org/pdf/1909.00871.pdf). arXiv preprint arXiv:1909.00871.\n",
        "  \n",
        "  - Shin, S., Song, K., Jang, J., Kim, H., Joo, W., & Moon, I. C. (2020). [Neutralizing Gender Bias in Word Embedding with Latent Disentanglement and Counterfactual Generation](https://arxiv.org/pdf/2004.03133.pdf). arXiv preprint arXiv:2004.03133.\n",
        "  \n",
        "  - Zhang, B. H., Lemoine, B., & Mitchell, M. (2018, December). [Mitigating unwanted biases with adversarial learning](https://dl.acm.org/doi/pdf/10.1145/3278721.3278779?casa_token=yd1KGvVDBGwAAAAA:YzUT7d8Fq4bOV2b5M-CB43NLqIReW7wx2EaZj0omJ0ncbZF_pkPFoyV6WHWIBnG_HKIRqiG7FWFjsA). In Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society (pp. 335-340). [Demo](https://colab.research.google.com/notebooks/ml_fairness/adversarial_debiasing.ipynb)\n",
        "  \n",
        "- **Fairness in Classification**\n",
        "  - Prost, F., Thain, N., & Bolukbasi, T. (2019, August). [Debiasing Embeddings for Reduced Gender Bias in Text Classification](https://arxiv.org/pdf/1908.02810.pdf). In Proceedings of the First Workshop on Gender Bias in Natural Language Processing (pp. 69-75).\n",
        "  \n",
        "  - Romanov, A., De-Arteaga, M., Wallach, H., Chayes, J., Borgs, C., Chouldechova, A., ... & Kalai, A. (2019, June). [What's in a Name? Reducing Bias in Bios without Access to Protected Attributes](https://arxiv.org/pdf/1904.05233.pdf). In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) (pp. 4187-4195).\n",
        "\n",
        "- **Grammatical Gender**\n",
        "  - Zhou, P., Shi, W., Zhao, J., Huang, K. H., Chen, M., & Chang, K. W. [Analyzing and Mitigating Gender Bias in Languages with Grammatical Gender and Bilingual Word Embeddings](https://aiforsocialgood.github.io/icml2019/accepted/track1/pdfs/47_aisg_icml2019.pdf). ICML 2019 - AI for Social Good. [Poster](https://aiforsocialgood.github.io/icml2019/accepted/track1/posters/47_aisg_icml2019.pdf)\n",
        "\n",
        "  - Zhao, J., Mukherjee, S., Hosseini, S., Chang, K. W., & Awadallah, A. [Gender Bias in Multilingual Embeddings](https://www.researchgate.net/profile/Subhabrata_Mukherjee/publication/340660062_Gender_Bias_in_Multilingual_Embeddings/links/5e97428692851c2f52a6200a/Gender-Bias-in-Multilingual-Embeddings.pdf).\n",
        "\n",
        "  - Gonen, H., Kementchedjhieva, Y., & Goldberg, Y. (2019). [How does Grammatical Gender Affect Noun Representations in Gender-Marking Languages?](https://arxiv.org/pdf/1910.14161.pdf). arXiv preprint arXiv:1910.14161.\n",
        "\n",
        "- **Other**  \n",
        "  - Zhao, J., Wang, T., Yatskar, M., Cotterell, R., Ordonez, V., & Chang, K. W. (2019, June). [Gender Bias in Contextualized Word Embeddings](https://arxiv.org/pdf/1904.03310.pdf). In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) (pp. 629-634). [slides](https://jyzhao.net/files/naacl19.pdf)\n",
        "\n",
        "\n",
        "##### Complete example of using `responsibly` with Word2Vec, GloVe and fastText: http://docs.responsibly.ai/notebooks/demo-gender-bias-words-embedding.html"
      ]
    },
    {
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      "source": [
        "## Bias in NLP\n",
        "\n",
        "Around dozen of papers on this field until 2019, but nowdays plenty of work is done. Two venues from back then:\n",
        "- [1st ACL Workshop on Gender Bias for Natural Language Processing](https://genderbiasnlp.talp.cat/)\n",
        "- [NAACL 2019](https://naacl2019.org/)"
      ]
    },
    {
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        "id": "muFnue4CNgN9"
      },
      "source": [
        ""
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