bolukbasi_et_al.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reproducing Bolukbasi et al. (2016)\n",
    "\n",
    "This reproduces a subset of the results from Bolukbasi et al.'s 2016 paper *Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings*. [[source]](https://arxiv.org/pdf/1607.06520.pdf)\n",
    "\n",
    "The authors show that gender bias is captured by a subspace in the word embedding and that gender-neutral words are linearly separable from words that are gendered by definition (gender-specific words). The authors also provide a methodology for modifying word embeddings to remove gender stereotypes without losing desired associations, which they refer to as debiasing."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 0: Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = [6, 4]\n",
    "import numpy as np\n",
    "\n",
    "from gensim.models import KeyedVectors\n",
    "from sklearn.svm import LinearSVC\n",
    "\n",
    "import helpers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Load and pre-process word vectors\n",
    "\n",
    "The paper uses the popular publicly-available 300-dimensional word2vec embedding trained on a corpus of Google News texts consisting of 3 million English words and terms. [[source]](https://code.google.com/archive/p/word2vec/)\n",
    "\n",
    "Bolukbasi et al. normalize the vector representations of the 50,000 most frequent words and discard all words that consist of at least 20 characters or include upper-case letters, digits, or punctuation were discarded. They keep a total of 26,377 words after filtering. On occasion they use the full set of word embeddings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26379"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load the roughly most frequent 50,000 words and normalize\n",
    "model = KeyedVectors.load_word2vec_format('GoogleNews-vectors-negative300.bin', binary=True, limit=50000)\n",
    "model.init_sims(replace=True)\n",
    "assert(np.linalg.norm(model['she']) == 1.0)\n",
    "\n",
    "# find short words without upper-case letters, digits, or punctuations\n",
    "clean_words = helpers.filter_model(model)\n",
    "len(clean_words)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sanity check for gender-specific words\n",
    "\n",
    "Bolukbasi et al. identify 218 gender-specific words whose definitions include female, male, woman, man, girl, boy, sister, brother, daughter, son, grandmother, grandfather, wife, or husband. We ensure that all of their gender-specific words are in the filtered set of word embeddings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(218,)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open('data/gender_specific_words.txt', \"r\") as file_object:\n",
    "    gender_words = file_object.read()[:-1]\n",
    "gender_words = np.asarray(gender_words.split(', '))\n",
    "\n",
    "assert(np.sum(np.isin(gender_words, clean_words, invert=True)) == 0)\n",
    "gender_words.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Selected word vector analogies\n",
    "\n",
    "Bolukbasi et al. found that many word vector analogies show gender bias and exhibit stereotypes. The following shows a few select word vector analogies. The comments are Bolukbasi et al.'s solutions to the analogies as mentioned in the paper."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "man : computer_programmer :: woman : homemaker\n",
      "man : king :: woman : queen\n",
      "father : doctor :: mother : nurse\n",
      "john : computer_programmer :: mary : graphic_designer\n",
      "he : coward :: she : slut\n",
      "she : grandmother :: he : grandfather\n"
     ]
    }
   ],
   "source": [
    "# we need the full model for this step\n",
    "full_model = KeyedVectors.load_word2vec_format('GoogleNews-vectors-negative300.bin', binary=True)\n",
    "full_model.init_sims(replace=True)\n",
    "\n",
    "analogies = [\n",
    "    ['man', 'computer_programmer', 'woman'], # homemaker\n",
    "    ['man', 'king', 'woman'], # queen\n",
    "    ['father', 'doctor', 'mother'], # nurse\n",
    "    ['john', 'computer_programmer', 'mary'], # homemaker - different!\n",
    "    ['he', 'coward', 'she'], # whore - different!\n",
    "    ['she', 'grandmother', 'he'], # grandfather\n",
    "]\n",
    "\n",
    "for a in analogies:\n",
    "    result = full_model.most_similar(positive=[a[2], a[1]], negative=[a[0]], topn=1)[0][0]\n",
    "    print('{} : {} :: {} : {}'.format(a[0], a[1], a[2], result))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generating analogies\n",
    "\n",
    "We can also generate analogies in the form of he : she :: x : y with Bolukbasi et al.'s method (described in Section 4). Since we only consider the most frequent 2000 words due to computational cost, we fail to find the more niche, biased analogies that Bolukbasi et al. mention in their paper. We show the ten highest-scoring analogies along with their score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3517"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analogies = helpers.generate_analogies('she', 'he', model, candidates=clean_words, num=2000)\n",
    "len(analogies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('she : he :: her : his', 0.9077807068824768),\n",
       " ('she : he :: woman : man', 0.7530544400215149),\n",
       " ('she : he :: daughter : son', 0.6747985482215881),\n",
       " ('she : he :: girl : boy', 0.6581321954727173),\n",
       " ('she : he :: mother : father', 0.6074056625366211),\n",
       " ('she : he :: spokeswoman : spokesman', 0.5979806184768677),\n",
       " ('she : he :: sister : brother', 0.5973289012908936),\n",
       " ('she : he :: girls : boys', 0.595537543296814),\n",
       " ('she : he :: daughter : father', 0.5538747310638428),\n",
       " ('she : he :: daughter : brother', 0.5529952645301819)]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analogies[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1: Identify the gender subspace\n",
    "\n",
    "Bolukbasi et al. use the centered difference vectors of ten pairs of gender words (defining sets) to identify the gender subspace. Applying PCA to the difference vectors reveals that the direction represented by the first eigenvector captures the majority of variance in these vectors.\n",
    "\n",
    "### Reproduce Fig. 6\n",
    "\n",
    "We show the explained variance for the first ten eigenvectors after applying PCA to (1) Bolukbasi et al.'s ten defining pairs for gender and (2) ten randomly sampled pairs averaged over 100 tries to reproduce Figure 6 in their paper. Bolukbasi et al. argue that the decrase in explained variance is much more gradual for (2), suggesting that the large drop in explained variance between the first and second eigenvector for (1) is significant.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
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     "output_type": "display_data"
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     "data": {
      "image/png": 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XvUd43uvPej339Xre/2/kD2MlSdc234yVpMYZ9JLUuHUd9OtxeYYkW5L8PMnpJKeSPDTumtZSkokkL/bf/VgXktyY5Kkkv+7/uX9y3DWthSRf6f8d/1WS/0jy9+OuaVzWbdCv4+UZLgFfraqPATuBB9bJef/VQ8DpcRexxr4P/KSqbgE+wTo4/ySbgAeB6ar6OL2HQfaOt6rxWbdBzzpdnqGqLlTVC/3Pf6L3j37TeKtaG0k2A3cBj427lrWS5Abg08DjAFV1sar+ONai1s4G4P1JNgDXsY7f4VnPQb8JODewPc86Cby/SrIVuBV4bsylrJXvAV8H3h1zHWvpI8AC8IP+LavHklw/7qJWW1X9DvgO8FvgAr13e3463qrGZz0HfeflGVqU5APAj4CHq+qtcdez2pJ8Hnijqp4fdy1rbANwG/BoVd0K/Blo/udRST5I73/o24APA9cnuWe8VY3Peg76Lks7NCnJ++iF/JNV9fS461kjtwNfSPIavdt0n0nyw/GWtCbmgfmq+uv/2p6iF/yt+yzwm6paqKp3gKeBT425prFZz0G/LpdnSBJ692tPV9V3x13PWqmqb1TV5qraSu/P+mdV1fwVXlW9DpxL8tF+0x3Aq2Msaa38FtiZ5Lr+3/k7WAc/hL6cLouaNekaWZ5hNdwO3Au8kuSlfts3q2pmfCVplX0ZeLJ/QXOW/hIlLauq55I8BbxA70mzF1nHSyG4BIIkNW4937qRpHXBoJekxhn0ktQ4g16SGmfQS1LjDHpJapxBL0mN+z/+YgPAkSsNTAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gender_pairs = [\n",
    "    ('she', 'he'),\n",
    "    ('her', 'his'),\n",
    "    ('woman', 'man'),\n",
    "    ('Mary', 'John'),\n",
    "    ('herself', 'himself'),\n",
    "    ('daughter', 'son'),\n",
    "    ('mother', 'father'),\n",
    "    ('gal', 'guy'),\n",
    "    ('girl', 'boy'),\n",
    "    ('female', 'male')\n",
    "]\n",
    "\n",
    "helpers.figure_6(model, k=10, sets=gender_pairs)\n",
    "helpers.figure_6(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find the gender subspace\n",
    "\n",
    "We can now find the gender subspace by applying PCA to the centered difference vectors as described in Section 6, Step 1. Looking at the cosine similarity of each defining pair to the subspace, we can see that some pairs such as ('herself', 'himself') are closer to the final subspace than others, e.g. ('female', 'male')."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('she', 'he'): similarity = 0.945\n",
      "('her', 'his'): similarity = 0.914\n",
      "('woman', 'man'): similarity = 0.821\n",
      "('Mary', 'John'): similarity = 0.567\n",
      "('herself', 'himself'): similarity = 0.949\n",
      "('daughter', 'son'): similarity = 0.749\n",
      "('mother', 'father'): similarity = 0.692\n",
      "('gal', 'guy'): similarity = 0.71\n",
      "('girl', 'boy'): similarity = 0.743\n",
      "('female', 'male'): similarity = 0.352\n"
     ]
    }
   ],
   "source": [
    "B = helpers.identify_subspace(gender_pairs, model, k=1, compare=True)[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1.5: Learn a classifier for gender-specific/neutral words\n",
    "\n",
    "Following Section 7 of Bolukbasi et al., we train a linear classifier to identify gender-specific and gender-neutral words. Our training data is the set of 26,379 filtered words, and we predict for the full set of 3 million word embeddings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of embeddings: 3000000\n",
      "Number of gender words: 6366\n"
     ]
    }
   ],
   "source": [
    "clf = LinearSVC(C=1.0, tol=0.0001)\n",
    "clf.fit(model[clean_words], np.isin(clean_words, gender_words))\n",
    "\n",
    "vocab = np.array(list(full_model.vocab.keys()))\n",
    "vectors = full_model[vocab]\n",
    "predictions = clf.predict(vectors)\n",
    "print('Number of embeddings: {}'.format(len(predictions)))\n",
    "print('Number of gender words: {}'.format(np.sum(predictions)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bolukbasi et al. identify a total of 6,449 gender-specific words. Some examples are below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['He', 'her', 'she', 'She', 'man', 'women', 'men', 'woman', 'wife',\n",
       "       'son', 'mother', 'father', 'daughter', 'husband', 'girls', 'girl',\n",
       "       'Her', 'boy', 'boys', 'brother', 'sister', 'Women', 'male',\n",
       "       'herself', 'brothers', 'dad', 'actress', 'mom', 'sons',\n",
       "       'girlfriend'], dtype='<U98')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# most common gender words\n",
    "vocab[predictions][:30]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Cindy_Iannarelli', 'Prince_Tamino', 'Alfred_Ginglen',\n",
       "       'idol_Marilyn_Monroe', 'Tima', 'Parvathamma', 'Sylvestor',\n",
       "       'Jerusalem_Shaare_Zedek', 'Huerkamp', 'Buyuk', 'Mahalko',\n",
       "       'Walburga', 'outfielder_Bobby_Higginson', 'nephews', 'paramour',\n",
       "       'Rani_Padmini', 'Arjun_Punj', 'Spanik', 'Emraan', 'Nijinsky_II'],\n",
       "      dtype='<U98')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# randomly sampled gender words\n",
    "np.random.choice(vocab[predictions], 20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reproduce Fig. 7\n",
    "\n",
    "In Figure 7, Bolukbasi et al. project selected words onto the vector difference between 'he' and 'she' (x-axis). The x-axis represents the decision boundary between gender-specific (below) and gender-neutral words (above). The absolute distance to the x-axis represents the distance to the decision boundary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "with open('data/figure_7_words.txt', \"r\") as file_object:\n",
    "    figure_7_words = np.array(file_object.read()[:-1].split(' '))\n",
    "\n",
    "plt.rcParams['figure.figsize'] = [10, 6]\n",
    "helpers.figure_7(figure_7_words, clf, model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2a: Hard debiasing\n",
    "\n",
    "Hard debiasing works by ***neutralizing*** gender-neutral words and ***equalizing*** gender-specific words with otherwise equivalent meanings. Soft debiasing is omitted for this work.\n",
    "\n",
    "Neutralized words are reembedded such that their vectors are orthogonal to the gender subspace. Equalized words are reembedded such that otherwise equivalent words such as 'father' and 'mother' only differ in the gender subspace, with all other dimensions being set to their average.\n",
    "\n",
    "### Examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# gender-neutral words exhibiting gender stereotypes\n",
    "stereotypes = [\n",
    "    ('midwife', 'doctor'),\n",
    "    ('sewing', 'carpentry'),\n",
    "    ('nurse', 'surgeon'),\n",
    "    ('softball', 'baseball')\n",
    "]\n",
    "\n",
    "# gender-specific words\n",
    "appropriate = [\n",
    "    ('her', 'his'),\n",
    "    ('she', 'he'),\n",
    "    ('mother', 'father'),\n",
    "    ('spokeswoman', 'spokesman')\n",
    "]\n",
    "\n",
    "B = helpers.identify_subspace(gender_pairs, model, k=1)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cosine Similarity with subspace before and after debiasing:\n",
      "('midwife', 'doctor'): before=0.3, after=0.0\n",
      "('sewing', 'carpentry'): before=0.39, after=0.0\n",
      "('nurse', 'surgeon'): before=0.37, after=0.0\n",
      "('softball', 'baseball'): before=0.44, after=0.0\n"
     ]
    }
   ],
   "source": [
    "_model = helpers.debias(stereotypes, B, model, equal=False, compare=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cosine Similarity with subspace before and after debiasing:\n",
      "('her', 'his'): before=0.91, after=0.99\n",
      "('she', 'he'): before=0.95, after=0.99\n",
      "('mother', 'father'): before=0.69, after=0.99\n",
      "('spokeswoman', 'spokesman'): before=0.68, after=0.99\n"
     ]
    }
   ],
   "source": [
    "_model = helpers.debias(appropriate, B, model, equal=True, compare=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fig. 7 after neutralizing gender-neutral words\n",
    "\n",
    "We neutralize all words that the classifier considers gender-neutral and plot them. We do not change the words that the classifier considers gender-specific since equalizing them requires manually assembling equality sets. Note that the classifier is imperfect: for example, 'he' is classified as gender-neutral. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# neutralize all gender-neutral words as determined by classifier\n",
    "N = [word for word in figure_7_words if word not in vocab[predictions]]\n",
    "new_model = helpers.neutralize(N, B, model)\n",
    "\n",
    "# keep gender-specific words as they are\n",
    "for word in figure_7_words:\n",
    "    if word not in new_model:\n",
    "        new_model[word] = model[word]\n",
    "new_model['he'] = model['he']\n",
    "\n",
    "helpers.figure_7(figure_7_words, clf, new_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that all words above the x-axis (i.e. words that the classifier considers to be gender-neutral and that we neutralized) are collapsed roughly onto the y-axis. There is a small discrepancy since the difference between 'she' and 'he' is only an approximation of the gender subspace."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}