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@abegong
Created August 3, 2016 03:39
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"\"\"\"\n",
"Replication code for my series on ethical algorithms\n",
"https://medium.com/@AbeGong/ethics-for-powerful-algorithms-1-of-3-a060054efd84#.d1d2z9a3b\n",
"\n",
"Abe Gong\n",
"August 2016\n",
"\"\"\"\n",
"\n",
"%matplotlib inline\n",
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib\n",
"import matplotlib.pyplot as plt\n",
"\n",
"plt.rcParams['figure.figsize'] = (10, 6)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"--2016-08-02 20:38:23-- https://raw.githubusercontent.com/propublica/compas-analysis/master/compas-scores-two-years.csv\n",
"Resolving raw.githubusercontent.com... 151.101.40.133\n",
"Connecting to raw.githubusercontent.com|151.101.40.133|:443... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 2546489 (2.4M) [text/plain]\n",
"Saving to: 'compas-scores-two-years.csv.2'\n",
"\n",
"100%[======================================>] 2,546,489 4.07MB/s in 0.6s \n",
"\n",
"2016-08-02 20:38:24 (4.07 MB/s) - 'compas-scores-two-years.csv.2' saved [2546489/2546489]\n",
"\n"
]
}
],
"source": [
"# Grab the data from ProPublica's github site.\n",
"! wget https://raw.githubusercontent.com/propublica/compas-analysis/master/compas-scores-two-years.csv"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(7214, 53)\n"
]
}
],
"source": [
"#Read it in\n",
"D = pd.read_csv('./compas-scores-two-years.csv')\n",
"print D.shape"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x1006351d0>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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PoQsAACABMl09INPVt9HJzFBnn6izX2S6gLl2XzNdC+eyGABAPvaMfxdkxXCI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\ncDZMRF0gAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10823f110>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Risk score histogram\n",
"D.v_decile_score.hist()\n",
"plt.title('Risk scores for all offenders')\n",
"plt.xlabel('Risk score')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x107dacb10>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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upvxERIwDJlT3146IE+ahRkkDwCOPwP77w0orwQMPwMUXw003wS67GMQkqS81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\nAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1081def90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"D.groupby('v_decile_score').two_year_recid.mean().plot()\n",
"plt.title('Recidivism by risk score decile')\n",
"plt.xlabel('Risk score')\n",
"plt.ylabel('Probability of recidivism')"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"African-American 3696\n",
"Caucasian 2454\n",
"Hispanic 637\n",
"Other 377\n",
"Asian 32\n",
"Native American 18\n",
"dtype: int64"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"D.race.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x108b99510>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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ZJ3QfTpkCDz6oIFZaCmMiIiJSKsuWwe23Q9u2YaqISZPgkUdgxx2jrqxqUhgTERGRhPzx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\nSJi0UHjTZfRC4au3rWb6W9PZcNMGFWIiIiKS0lK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"text/plain": [
"<matplotlib.figure.Figure at 0x107b28750>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"D[D.race==\"African-American\"].groupby('v_decile_score').two_year_recid.mean().plot()\n",
"D[D.race==\"Caucasian\"].groupby('v_decile_score').two_year_recid.mean().plot()\n",
"plt.title('Recidivism by risk score decile, conditional on race')\n",
"plt.xlabel('Risk score')\n",
"plt.ylabel('Probability of recidivism')\n",
"plt.legend(['Black', 'White'], loc='lower right')"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#Get recidivism proportions and counts by race and risk score decile.\n",
"p_afram = D[D.race==\"African-American\"].groupby('v_decile_score').two_year_recid.mean()\n",
"n_afram = D[D.race==\"African-American\"].groupby('v_decile_score').two_year_recid.count()\n",
"\n",
"p_cauca = D[D.race==\"Caucasian\"].groupby('v_decile_score').two_year_recid.mean()\n",
"n_cauca = D[D.race==\"Caucasian\"].groupby('v_decile_score').two_year_recid.count()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x108c78fd0>"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/abe/anaconda/lib/python2.7/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
" if self._edgecolors == str('face'):\n"
]
},
{
"data": {
"image/png": 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0d8PatcNnsu+eqAyXJGkO6e/vp7+/f8rnNzS1RUQsAvYEnjq4LzMvn+Cc+cBP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\npBb6/wFcQ3Rud6328wAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x108a5a210>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Plot the thing with 1-SD error bars\n",
"x = np.arange(1,11)\n",
"sd_afram = 1.96*np.sqrt(p_afram*(1-p_afram)/n_afram)\n",
"plt.vlines( x, p_afram+sd_afram, p_afram-sd_afram, color='black')\n",
"a = plt.scatter( x, p_afram, color='black', label=\"Black\")\n",
"\n",
"sd_cauca = 1.96*np.sqrt(p_cauca*(1-p_cauca)/n_cauca)\n",
"plt.vlines( x+.1, p_cauca+sd_cauca, p_cauca-sd_cauca, color='blue')\n",
"b = plt.scatter( x+.1, p_cauca, color='blue', label=\"White\")\n",
"\n",
"plt.ylabel('Probability of two-year recidivism')\n",
"plt.xlabel('COMPAS score decile')\n",
"plt.legend(loc='lower right')"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x108f85bd0>"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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UFLjiiuBqyyefjMkuRIqdqA2JYWZHhX3JKmS2XOOUiYgUjFtvvZUjjjiCgQMH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\noKRMREREJAooKRMRERGJAv8fJeuhPsC2L7YAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x108ef3a50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"X = np.arange(1,11)\n",
"plt.plot(X, 100/(1+np.exp(.5*(5-X+np.random.uniform(size=10)))), color='blue')\n",
"plt.plot(X, 100/(1+np.exp(.5*(5-X+np.random.uniform(size=10)))), color='black')\n",
"plt.title(\"Hypothetical conditional effect plot with no racial bias\")\n",
"plt.ylabel('Probability of two-year recidivism')\n",
"plt.xlabel('COMPAS score decile')\n",
"plt.xlim(0,12)\n",
"plt.ylim(0,100)\n",
"plt.legend([\"White\", \"Black\"], loc='lower right')"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x109b48b10>"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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27OGRRx5h8uTJTJ8+nTPPPDPsSCJJLZqibDnwW7yDiIjk1/Tp0KwZjBgBF14Y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\niEgMUCgTERERiQEKZSIiIiIx4H8BojKprvpSaV0AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x108f3d650>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"X = np.arange(1,11)\n",
"plt.plot(X, 100/(1+np.exp(.5*(3-X+np.random.uniform(size=10)))), color='blue')\n",
"plt.plot(X, 100/(1+np.exp(.5*(5-X+np.random.uniform(size=10)))), color='black')\n",
"plt.title(\"Hypothetical conditional effect plot, biased against blacks\")\n",
"plt.ylabel('Probability of two-year recidivism')\n",
"plt.xlabel('COMPAS score decile')\n",
"plt.xlim(0,12)\n",
"plt.ylim(0,100)\n",
"plt.legend([\"White\", \"Black\"], loc='lower right')"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x109e0ff10>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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6+uuvmTx58p4kbPHixRxxxBG5ErBjjjmm0MsURYtzsGhR7gRs3jw48kifhJ1w\ngv/bvLkK90WSnZIyEZFimD9/PieccAJ9+vTZk4C1bt2aSnG6AveWLfDtt7mTsIoV9yZfJ5wAHTvC\nAQfEJRwRiaKUSMrM7F7gMiAH+B7oC1QDPgQOBRYDFzjnNuZ5n5IyEYmqp59+mvnz5/Paa6/FfFk5\nOX4E/fAEbMECaN/eJ2ChJKxx45iHIiJxkPRJmZk1A8YCbZxzO83sQ2AEcBSw1jn3hJndA9R2zvXP\n814lZSISVd27d+fWW2+ld+/eUZ/3hg0wbdreBGzaNKhdO3cC1q4dxKlRTkTiLBWSsjrAFKALsAX4\nDPgP8AJwinNulZkdDGQ651rnea+SMhGJmo0bN9KkSRNWrlxJtf285lB2Nsydm7sV7Lff4Ljj9iZg\nnTvDQQdFKXgRSXpJP06Zc269mT0NLAW2A6Occ6PN7CDn3KrgZasAHbpEJKa+/PJLTj755BIlZKtX\n702+pk71dWGHHLK3FezWW+Goo3RJIxGJXNwPF2bWErgVaAZsAj42s8vCX+Occ2amJjERiamMjAx6\n9epV5Ot27fJjgYW3gq1b51u+unTxlzHq1AliNDasiJQRifgf7jhgsnNuHYCZDQFOAFaa2cHOuZVm\n1hBYnd+bH3rooT3309LSSEtLi3nAIlL6ZGdnM2LECB5++OF9pi1fnjsBmz0bWrTwCdhpp8H998MR\nR0CUro4kIqVEZmYmmZmZJX5/ImrK2gHvA8cDO4C3gOn4sy7XOeceN7P+QC0V+otIrEyZMoVrr72W\n77//nm++gQkT9iZhO3bkLsY/7jioWTPREYtIqkn6Qn8AM7sbuBI/JMZM4GqgBvAR0BQNiSEiMXbf\nffeRk5NDvXqP8dxzfmT8UBLWooUGZhWR/ZcSSVlJKSkTkWhp3749aWkvMnz4SYwfr7HBRCT6kv7s\nSxGRRFu2bBkLFixjw4YuSshEJGmoTFVEypz77svAuR6MHVuBZs0SHY2IiKekTETKlPffh48+SufR\nR8+iZctERyMispdqykSkzPj4Y7jppm1s3Xowy5YtoXbt2okOSURKseLWlKmlTETKhKFD4aab4L77\nxnHssR2UkIlI0lFSJiKlXkYGXHcdjBgB8+alc9ZZZyU6JBGRfejsSxEp1b78Evr2heHDoUMHR0ZG\nBqNGjUp0WCIi+1BLmYiUWuPGwWWXwZAh/jqV33//PRUqVKB169aJDk1EZB9KykSkVPr6a7jwQvjo\nIzjpJP9mVxUjAAAgAElEQVRcerrvujQN1y8iSUhJmYiUOtOmwbnn+uEv0tL2Ph9KykREkpGGxBCR\nUmXGDOjZE9580/8NWbt2LS1btmT16tVUrlw5cQGKSJmhITFEpMyaMwd69YLXXsudkAF88cUXdO/e\nXQmZiCQtJWUiUirMnQtnngkvvADnnLPvdHVdikiyU/eliKS8+fOhe3d48km45JJ9p+/evZsGDRow\nb948GjZsGP8ARaRMKm73ZcTjlJlZzfDXO+fWFzM2EZGoW7AA/vQn+Ne/8k/IACZNmkTLli2VkIlI\nUisyKTOz64CHgZ1ATvC0A1rEMC4RkSItXgynnQYPPAB9+hT8OnVdikgqKLL70swWAF2cc2vjE1Kh\nsaj7UkQAWLYMTjkFbr8dbryx8Ne2bt2a9957j+OOOy4+wYmIEJuzLxcC20sekohIdK1Y4WvIbryx\n6IRswYIFbNq0iY4dO8YnOBGREoqkpqw/MMXMpgC7guecc+7m2IUlIpK/Vat8l+VVV/lWsqJkZGTQ\ns2dPypXTyeYiktwiScpeA74CvsfXlBm+pkxEJK7WrvVF/RddBPfeG9l70tPTueGGG2IbmIhIFERS\nUzbLOdchTvEUSjVlImXX+vW+haxHD3+mZSSXr9yyZQuHHHIIK1asoEaNGrEPUkQkTCxqyr4ws+vM\nrKGZ1Qnd9iNGEZFi2bQJzjjD15FFmpABjB49mhNPPFEJmYikhEi6Ly/Bd1f2z/N88+iHIyKS25Yt\nfqT+Ll3gqaciT8jAd1326tUrdsGJiESRRvQXkaT1xx++u7JNG3jlleIlZDk5OTRs2JDJkyfTsmXL\n2AUpIlKAqHdfmtn5wWj+mNkDZjbEzHRuuYjE1Pbt0Ls3tGwJL79cvIQM4Ntvv6Vu3bpKyEQkZURS\nU/YP59xmMzsJOA0YCLwS27BEpCzbsQP+53/g4IPhjTegJKNZZGRkqOtSRFJKJIe67ODvWcDrzrl0\noGLsQhKRsmzXLjj/fKhZE95+G8qXL9l8dGklEUk1kQyJkQEsB/4MdAB2ANOcc+1iH94+saimTKQU\n270bLrwQcnLg44+hYgn//VuxYgVt27Zl1apVVCzpTERE9lMshsS4ABgFnO6c2wjUBu4qYXwiIvnK\nyoLLL4edO+HDD0uekAGMGDGCM844QwmZiKSUApOyUHE/UBkYB6wLxifbCXwbh9hEpIzIzvaXTVq/\nHj79FCpX3r/5aSgMEUlFBXZfmlmGc66XmS1m38sqOedci1gHl09M6r4UKWVycuDaa+HXXyEjA6pW\n3b/57dixgwYNGrBw4ULq1asXnSBFREqguN2XBQ4e65zrFfxtFoW4RET24RzceCP89BOMHLn/CRlA\nZmYmxxxzjBIyEUk5kYxTNtzMLjGzavEISETKBufgtttg5kwYMQKqV4/OfDMyMnTWpYikpEgK/Z8G\nTgbmmdmnZvZXM6sS47hEpBRzDu65ByZO9C1kNWsW/Z7I5utUTyYiKavIa1865zKBTDOrAJwKXIMf\nQDZKh1ERKWv+8Q8YNQrGjoVataI333nz5pGTk0Pbtm2jN1MRkTiJ5ILkmNkBQG/88BgdgbdjGZSI\nlF6PPgpDhkBmJtStG915hwaMteJek0lEJAlEUlP2EfAT0B14EWjlnLsp1oGJSOnzxBPw3nswZgzU\nrx/9+aueTERSWSQj+p8BfOWcyy70hXGgITFEUtdzz8GLL8L48dCoUfTnv379epo1a8aqVas44IAD\nor8AEZFiitqQGGZ2mnNuDFAdOCesO8Dw45QN2a9IRaTMeOkleP752CVkACNHjiQtLU0JmYikrMJq\nyroBY4Cz2XfwWAAlZSJSpDfegH//29eQNW0au+Wo61JEUl2R3ZfJRN2XIqnlnXfg73+HcePgsMNi\nt5ysrCwOOugg5syZQ+PGjWO3IBGRYohm9+Udwd18syDn3DPFjE1EypDBg6F/f1/UH8uEDGDKlCk0\nbdpUCZmIpLTCui9r4BOyI4Djgc/x9WRnAdNjH5qIpKpPP4Vbb4XRo6FNm9gvLzQUhohIKovk7MuJ\nQE/n3JbgcQ1ghHPu5DjElzcWdV+KJLnhw+Hqq/1I/R06xGeZbdu2ZcCAAXTu3Dk+CxQRiUDUui/D\nNAB2hz3eHTwnIpLLyJHQrx9kZMQvIVu0aBGrV6/m+OOPj88CRURiJJKk7B1gupkNwXdf/gWN6C8i\neQwfDlddBcOGQTzzo4yMDHr27Em5cpFcyldEJHlFcu3Lf5nZSPxFyR3Qxzk3K+aRiUhKWLkSbr8d\npkyBoUPhxBPju/z09HSuvvrq+C5URCQGCqwpM7OazrnNZlYn9FTw1wE459bHIb68MammTCRJ5OTA\n66/D/ff7Lst//AOqVo1vDFu3bqVhw4YsX76cmjVrxnfhIiJFiGZN2QdAL2Am+Q+L0byYsYlIKfH9\n93Dddf7+2LFw9NGJiWPMmDF06tRJCZmIlAoFJmXOuV7B32Zxi0ZEktq2bfDIIzBgAPzzn3DNNZDI\nUi4NhSEipUmRh1Mz+x8zqxX2uJaZ/SW2YYlIsvniC2jbFpYu3dtSlsiEzDmnSyuJSKkSydmXDznn\nPgs9cM5tNLOHgKExi0pEksaKFX4g2Bkz4JVX4PTTEx2RN2vWLGrUqMFhsb5cgIhInETyf25+BWrl\nox2IiCSX7Gx46SVo185fJumHH5InIQPfddmrV69EhyEiEjWRtJTNMLNngP/iE7S/ATNiGpWIJNTs\n2b57slIlyMyEo45KdET7Sk9P59///neiwxARiZpIWspuwo/i/yEwGNiBT8xEpJTZuhXuvNO3iF17\nLYwfn5wJ2apVq/jll1846aSTEh2KiEjURDJ47FbgHjOr5pz7Iw4xiUgCpKfDjTfCySf7rsoGSXwx\ntREjRvCnP/2JSpUqJToUEZGoKTIpM7MTgTeAGkATM2sHXOecuyHWwYlI7C1fDjffDN99B2+8AX/6\nU6IjKlp6ejq9e/dOdBgiIlEVSfflc8CZwFoA59wc4JRYBiUisZedDf/5jy/kP+ooP8xFKiRkO3fu\n5KuvvqJHjx6JDkVEJKoiKfTHObfULNdJmFn7s9Bg3LM3gKPwVwvoC/yCr1s7FFgMXOCc27g/yxGR\n/M2c6Qv5q1aFiROhTZtERxS5iRMncuSRR9IgmftXRURKIJKWsqVm1hXAzCqZ2Z3Aj/u53OeBEc65\nNsAxwE9Af2C0c+5wYEzwWESiaMsWuO026NED/vY3f2ZlKiVkoKEwRKT0iiQp+1/82ZaNgOVAB/bj\n7EszOxA42Tk3EMA5l+Wc2wT0Bt4OXvY2oKsGiETR0KG+m3LDBl/I36cPWMSXyU0OzjldWklESq1C\nuy/NrALwvHPukiguszmwxszeBNrhxzy7FTjIObcqeM0q4KAoLlOkzFq2DG66CX78Ed5+G049NdER\nldzPP//Mjh07aNeuXaJDERGJukJbypxzWcChZlY5isusAHQEXnLOdQT+IE9XpXPO4WvNRKSEsrLg\n2WehQwfo2NGfXZnKCRnsvQC5pVoTn4hIBCIp9F8EfG1mnwPbguecc+6ZEi7zN+A359w3weNPgHuB\nlWZ2sHNupZk1BFbn9+aHHnpoz/20tDTS0tJKGIZI6fXtt37w11q1YNIkOOKIREcUHenp6dx+++2J\nDkNEJF+ZmZlkZmaW+P3mG6UKeYG/+Djkablyzj1c4oWaTQCuds79HMy/ajBpnXPucTPrD9RyzvXP\n8z5XVLwiZdnmzXD//fDRR/Dkk3DZZalXN1aQjRs30qRJE1atWkXVqlWLfoOISIKZGc65iI/CkYzo\n/9B+RZS/m4D3zawS8Ct+SIzywEdm1o9gSIwYLFekVHIOhgyBW26BM86AuXOhbt1ERxVdX375Jd26\ndVNCJiKlVkTjlEVbMADt8flMSoGhK0WSy5Il/vJIv/4KgwZBt26Jjig2dNaliJR2kQyJISJJaPdu\neOopOPZY6NIFZs8uvQlZdnY2X3zxhcYnE5FSraghMcoDNzvnno1TPCISgalT/Yj8DRr4+61aJTqi\n2Jo2bRoNGzakadOmiQ5FRCRmihoSIxuI5hhlIrIfNm3yI/H/z//APffAl1+W/oQMICMjQ12XIlLq\nRdJ9+bWZvWhmJ5tZx9At5pGJyB7O+TMqjzzSjz82bx5ccknpObOyKKonE5GyIJIhMTLJZyBX51zc\nh6HUkBhSFi1aBDfc4Efmf/VV6No10RHF19KlS+nYsSOrVq2ifPnyiQ5HRCRisRgSI22/IhKREtm9\nG555Bp54Au66C26/HSpVSnRU8TdixAh69OihhExESr2IhsQws7OAI4Eqoeecc4/EKiiRsm7yZF/I\n36gRfPMNtGiR6IgSJz09ncsvvzzRYYiIxFwk3ZevAgcA3YHXgfOBac65frEPb59Y1H0ppVpWlr94\n+LBh/rqVF1xQdurG8rNt2zYOPvhgli5dSq1atRIdjohIsUS9+xI40Tl3tJl955x72MyeBkaWPEQR\nKcgDD8CCBb6QXzkIjB07lo4dOyohE5EyIZKkbHvwd5uZNQLWAQfHLiSRsmnECHjvPZg5UwlZiIbC\nEJGyJJKkbLiZ1QaeBGYEz70eu5BEyp5ly+Cqq+CTT6B+/URHkxycc6SnpzN69OhEhyIiEhdF1pTl\nerFZFaCKc25j7EIqdPmqKZNSZ/duSEuDs8+G/v0THU3ymDNnDueeey4LFizAynJhnYikrOLWlBU5\neKyZVTOzB8zsdefcDqB+cDamiETBAw9AzZpw992JjiS5hAaMVUImImVFJCP6vwnsAk4MHq8A/hWz\niETKkBEjYNAgePddKBfJt7EMUT2ZiJQ1kfwMtHTOPY5PzHDO/RHbkETKhlAd2aBBUK9eoqNJLmvW\nrGHu3Ll069Yt0aGIiMRNJEnZTjM7IPTAzFoCO2MXkkjpt3s3XHQR3HYbnHRSoqNJPl988QWnnXYa\nlStXTnQoIiJxE8nZlw/hxyVrbGaDgK5AnxjGJFLq3X8/HHigv3yS7EtdlyJSFkV09qWZ1QO6BA+n\nOefWxDSqguPQ2ZeS8jIy4Prr/Xhk6rbc1+7du2nQoAE//vgjBx+sIRFFJHVFfUR/MxsCDABGOOdy\n9ic4kbJu2TLo18+PR6aELH9ff/01hx12mBIyESlzIqkpexm4FFhgZv82syNiHJNIqbR7N1x4oerI\nipKenk6vXr0SHYaISNwVmZQ550Y75y4BOgKLgTFmNtnM+ppZxVgHKFJa3Hcf1K6tOrKiqJ5MRMqq\nSAr9MbO6wOXAZcBMYBBwEnAlkBar4ERKi/R0GDzY15FpPLKC/fLLL2zevJkOHTokOhQRkbiLpKbs\nM6A18C5wtnPu92DSYDObUfA7RQRg6VJfRzZkiOrIipKRkUHPnj0pp8xVRMqgSFrKXnDOjc1vgnPu\n2CjHI1KqhMYju+MO6No10dEkv/T0dG688cZEhyEikhDFvSB5unMuYcUeGhJDUs1dd8G8eTB8uLot\ni7J582YaNWrE77//TvXq1RMdjojIfov6kBh5NCrm60XKrOHD4aOPVEcWqdGjR9O1a1clZCJSZhU3\nKZsdkyhESpmlS+Hqq+Gzz6Bu3URHkxo0FIaIlHURd1+aWfXg9VtiG1KhMaj7UpLe7t3QrRuce66G\nv4hUTk4ODRs2ZMqUKbRo0SLR4YiIREVxuy+L7FQxs6PNbBYwD5hrZjPMrO3+BClSmv3977517I47\nEh1J6vj222+pV6+eEjIRKdMi6b58DbjdOTcOwMzSgudOjGFcIilp+HD48EOYNUt1ZMWRnp6uAWNF\npMyL5GejaighA3DOZQLVYhaRSIpassTXkQ0erDqy4lI9mYhIZC1li8zsAfzgsYa/DubCmEYlkmJ2\n7fLXtbzrLjhRbcjFsnz5chYvXsyJ2nAiUsZF0lJ2FdAAGAJ8CtQPnhORwN//DvXrw+23JzqS1DNi\nxAjOPPNMKlQo7sngIiKlSyRHwdrOuZtiHolIivr8c/j4Y41HVlLp6elceOGFiQ5DRCThihwSw8wm\nAI2B6cBEYIJz7vs4xJZfLBoSQ5LKkiXQqRMMHQonnJDoaFLPjh07aNCgAYsXL6ZOnTqJDkdEJKqi\nPqK/c66bmVUGjgPSgAwzq+6c0xFUyrTwOjIlZCWTmZlJu3btlJCJiBBBUmZmJwHdgJOAWkAGMCHG\ncYkkvXvvhQYNNB7Z/tBQGCIie0VSUzYemAE8Boxwzu2MbUgiyW/YMPj0U19HZhE3TEs45xzp6emk\np6cnOhQRkaQQSVJWF99KdjJws5llA1Odc/fHNDKRJLV4MVx7ra8jU69byc2dOxeAo446KsGRiIgk\nh0hqyjaa2UJ8sX8T/Ej+lWIdmEgyCtWR3X236sj2V0ZGBmeddRampkYRESCya18uBJ4G6gAvAYc7\n57rFOjCRZNS/Pxx0kMYjiwbVk4mI5BbJkBjdnHMT8jzX1Tk3KaaR5R+LhsSQhBk2DG65xdeRqdty\n/6xbt47mzZuzevVqqlSpkuhwRERiorhDYkQy1OVz+Tz3YuQhiaS+UB3Z4MFKyKJh1KhRnHrqqUrI\nRETCFFhTZmYn4OvHGpjZ7fjrXgLUILJkTqRUCNWR3XMPdOmS6GhKB3Vdiojsq7DkqhI+ASsf/K0e\n3DYDf419aCLJIVRHdtttiY6kdMjKymLkyJH07Nkz0aGIiCSVAlvKnHPjgfFm9pZzbnH8QhJJHkOH\nwpAhGo8smiZPnkyzZs1o1KhRokMREUkqkQyJsTgOcYgknUWLfB3Z55+rjiyaQkNhiIhIbqoNE8lH\nqI7s3ntVRxZtqicTEclfgUmZmT0e/L0gfuGIJId77oGGDeHWWxMdSemycOFC1q5dy3HHHZfoUERE\nkk5hLWW9zA+1fW+8ghFJBkOHwmefwZtvqo4s2jIyMujZsyflyqmRXkQkr8Jqyr4ANgDVzWxLnmnO\nOVczdmGJJEaojmz4cNWRxUJGRgbXXHNNosMQEUlKkYzo/7lzrnec4imURvSXWNq1C046CS6+WMNf\nxMLWrVtp2LAhy5cvp2ZN/U8nIqVfcUf0j+Tsy95mdhBwfPDUdOfc6pIGKJKs7r4bDjlEdWSx8tVX\nX9G5c2clZCIiBYjkguQXANOBC4ALgelmdn6sAxOJp88+89e2VB1Z7GgoDBGRwkXSffkd8KdQ65iZ\n1QfGOOeOiUN8eWNR96VE3aJF0LkzpKdDp06JjqZ0ysnJoXHjxkyYMIFWrVolOhwRkbiIxQXJDVgT\n9ngde6+DKZLSdu6ECy6Av/9dCVkszZo1i5o1ayohExEpRJE1ZcBIYJSZDcInYxfiz8wUSXl33w2N\nGsEttyQ6ktItPT2dXr16JToMEZGkFkmh/11mdh7QNXjqVefcZ7ENSyT2hgzxl1DSdS1jLyMjg8cf\nfzzRYYiIJLUia8pitmCz8sC3wG/OubPNrA7wIXAosBi4wDm3Mc97VFMmUbFwob98kurIYm/lypW0\nadOG1atXU7FixUSHIyISN7GoKYuVW4B5QCjL6g+Mds4dDowJHotE3c6d/rqW992nhCweRowYwemn\nn66ETESkCAlJysysMdATeIO9Jw30Bt4O7r8N/CUBoUkZcNdd0Lgx3HxzoiMpG1RPJiISmUjGKett\nZtFO3p4F7gJywp47yDm3Kri/CjgoyssU4dNP/SWUBg5UHVk87Ny5kzFjxtCjR49EhyIikvQiSbYu\nBBaY2RNm1np/F2hmZwGrnXOzKGBojaBwTMVjElULF8L118OHH0Lt2omOpmyYMGECRx11FPXr1090\nKCIiSS+Ssy8vNbMDgYuBt8zMAW8CHzjn8l6oPBInAr3NrCdQBahpZu8Cq8zsYOfcSjNrCOR7KaeH\nHnpoz/20tDTS0tJKEIKUNaHxyFRHFl/p6ekaxV9EyozMzEwyMzNL/P6Iz740s3rA5cCt+AL9w4D/\nOOf+U+KFm50C3BmcffkEsM4597iZ9QdqOef653m9zr6UErn5ZvjtN999qW7L+HDO0apVK4YMGUK7\ndu0SHY6ISNxF/YLkZnYO0AefhL0DHO+cW21mVfHJWYmTskAoy/o38JGZ9SMYEmM/5ysCwCef+KEv\nNB5ZfM2fP59du3ZxzDFxvyKbiEhKimRE/3OBZ51zE8KfdM5tM7Or92fhzrnxwPjg/nrgT/szP5G8\nfv3V15GNGAG1aiU6mrIl1HVpyoRFRCISSaH/qrwJmZk9DuCc+yomUYlEQaiO7IEH4PjjEx1N2aOh\nMEREiqfImjIzm+Wc65Dnue+dc0fHNLL8Y1FNmUTspptgxQrffanGmvjauHEjTZs2ZeXKlVStWjXR\n4YiIJETUasrM7HrgBqClmX0fNqkGMKnkIYrElnPw2mu+y3LGDCVkiTBq1Ci6deumhExEpBgKqykb\nBHyBL8C/h71jim1xzq2LdWAiJfHDD3DjjbBpEwwbpjqyRNFQGCIixVdg96WZ1XTObTazuuQzkGtQ\nmB9X6r6UgmzeDA89BO+95/9edx2UL5/oqMqm7OxsDjroIGbOnEnTpk0THY6ISMJEc0iMD4BewAzy\nH12/eTFjE4k652DQILj7bjjzTJg7FzR4fGJNmzaNRo0aKSETESmmApMy51yv4G+zuEUjUgzff++7\nKrdu9YPCdumS6IgE1HUpIlJShRX6dyzsjc65mdEPR6RomzbBgw/6FrJHHoFrrlFXZTJJT0/n1Vdf\nTXQYIiIpp7Duy2co/KLgp0Y5FpFCOedrxu65B3r1gnnzoF69REcl4ZYuXcrvv/9OJ11gVESk2Arr\nvkyLYxwihZozx3dV7tgBQ4fqouLJKiMjgx49elBeTZciIsVWWPdld+fcWDM7j/zPvhwS08hEgI0b\n4R//gA8/hEcfhX791FWZzNLT07nyyisTHYaISEoqrPvyFGAscDb5d2MqKZOYycmBd9+F/v2hd2/f\nVVm3bqKjksJs27aNiRMn8v777yc6FBGRlFRY9+WDwd8+cYtGBJg9G/72N9i9Gz7/XNetTBVjx47l\n2GOPpZZG7BURKZEiL0huZvXM7AUzm2VmM83s+WBAWZGo2rjRX6/yjDOgb1+YOlUJWarYvHkzjz76\nKOeff36iQxERSVlFJmXAYGA1cC7wV2AN8GEsg5KyJScH3nwT2rSBrCz48Ue4+mooF8neKQm3ZcsW\nevTowbHHHsv111+f6HBERFJWgZdZ2vMCsx+cc23zPPe9c+7omEaWfyy6zFIpM3Om76p0Dl58EY47\nLtERSXFs3bqVHj16cNRRR/HSSy9RTpm0iMgexb3MUiRH0C/N7GIzKxfcLgS+LHmIIrBhg0/Gevb0\nrWKTJyshSzVbt26lZ8+etG7dWgmZiEgUFHZB8q3sPeuyGpAT3C8H/OGcqxH78PaJSS1lKS7UVXnf\nfXDeeX6Yizp1Eh2VFNcff/xBr169aNmyJa+//roSMhGRfETtguTOuerRCUnEmzHDt46ZwYgR0LHQ\nC3lJstq2bRtnn302zZs3V0ImIhJFhY1TtoeZ1QYOA6qEnnPOTYhVUFK6rFvnW8aGDoXHHoMrr1QR\nf6ravn07vXv3pkmTJrzxxhtKyEREoiiSITGuASbg68geBkYBD8U2LCkNcnLg9dfhyCOhYkV/VmXf\nvkrIUtX27ds555xzaNiwIQMHDtSllEREoiySlrJbgOOBKc65U82sNfBYbMOSVPfNN76rsmJFGDUK\n2rdPdESyP3bs2MFf/vIX6tWrx1tvvaWETEQkBiJps9jhnNsOYGZVnHM/AUfENixJVevWwXXX+Usj\n/e1vMHGiErJUt3PnTs4991xq167NO++8o4RMRCRGIknKlgU1ZUOB0Wb2ObA4plFJysnOhldf9V2V\nVar4rkrVjqW+nTt3ct5551G9enXee+89KlSIqAxVRERKoMjBY3O92CwNqAmMdM7tilVQhSxfQ2Ik\noenT4YYb4IAD/ACw7dolOiKJhl27dvHXv/6VSpUq8cEHH1CxYsVEhyQiklKiNiRGnpkeC5yEH7fs\n60QkZJJ81q6Fe++FjAx4/HG47DI/3IWkvl27dnHBBRdQoUIFJWQiInESydmX/wDeAuoA9YA3zeyB\nGMclSSw7G15+2XdVVq/uuyovv1wJWWmxe/duLrroIgAGDx6shExEJE4iufblz8AxzrkdweMDgDnO\nucPjEF/eWNR9mWBTp/oC/mrV4L//haPjfgVUiaXdu3dz8cUXs2vXLj755BMqVaqU6JBERFJWLLov\nlwMHADuCx1WA30oQm6SwNWugf3/44gt48km45BK1jJU2WVlZXHrppezYsYNPP/1UCZmISJwVmJSZ\n2QvB3U3AXDMLXYT8z8D0WAcmySE7G155BR5+2NeM/fQT1KyZ6Kgk2rKysrjsssvYunUrn332GZUr\nV050SCIiZU5hLWUz8IX93+KHwwj1G2aG3ZdSaMsW3005aRIMGeIvGD52LLRtm+jIJBaysrK44oor\n2LhxI0OHDlVCJiKSIBENiWFmlYFQDdlPzrndMY2q4DhUUxZlzsHSpT4BmzQJJk+GX37xFwvv2hXS\n0uD009VVWVplZ2dz5ZVXsnr1aoYNG8YBBxyQ6JBEREqN4taURVLonwa8DSwJnmoKXOmcG1/SIEtK\nSdn+y8qCOXP2JmGTJsHu3T4BC906dgSVE5V+2dnZ9O3blxUrVjB8+HAlZCIiURaLpGwmcLFzbn7w\n+HBgsHOu435FWgJKyopv0yaYMsW3gE2a5K9J2bQpnHji3iSsZUu1hJU1OTk59OvXjyVLlpCenk7V\nqlUTHZKISKkTi7MvK4QSMgDn3M9mpmutJCHnYPHi3K1gCxfCccf55OuOO+CEE6B27URHKomUk5PD\nNddcw+LFi5WQiYgkkUhayt4EsoH3AAMuBco5566KfXj7xKKWsjC7d8Ps2bmTMOdyd0W2b6+uSNkr\nJyeH6667jp9//pkRI0ZQrVq1RIckIlJqxaL7sjJwI9A1eGoi8JJzbmeJoyyhsp6UbdjguyJDCdi3\n31cUfiIAABo4SURBVEKLFj75CnVHNm+urkjJX05ODtdffz3z5s3jiy++oHr16okOSUSkVItqUhZ0\nU/7gnGsdjeD2V1lKypzzXY/hrWBLlsDxx+9tBevSBWrVSnSkkgqcc/ztb39jzpw5jBw5kho1aiQ6\nJBGRUi+qNWXOuSwzm29mhzrnlhT2Wtk/u3bBzJm5h6YoX35vAnbNNdCuHegyhFJczjluuukmZs+e\nrYRMRCSJRdJ9ORHogB/F/4/gaeec6x3j2PKLpdS0lK1fv/eMyEmTfELWqlXuerCmTdUVKfvHOcct\nt9zC9OnTGTVqFAceeGCiQxIRKTNiUVN2Suhu2NNO45RFzjk/IGt4K9hvv0GnTrm7InX5Iokm5xy3\n3XYbkydPZvTo0UrIRETiLGpJmZkdAPwv0Ar4DhiYqJH8w2JKyaSsf38YNCh3Qf4xx0AFDSwiMeKc\n484772T8+PF89dVX1FLxoYhI3EUzKfsI2IU/27InsNg5d0tUoiyhVE3Kdu3SsBQSP8457r77bsaO\nHctXX31FbQ1MJyKSENFMyr53zh0d3K8AfOOc6xCdMEsmVZMykXhxznHvvfcyatQoxowZQ506dRId\nkohImVXcpKxcIdOyQnecc1mFvE5EkoBzjvvuu4+RI0fy1VdfKSETEUkxhbWUZQPbwp46ANge3HfO\nubiXpaulTCR/zjn+8Y9/MGzYMMaOHUu9evUSHZKISJkXtXHKnHPloxOSiMTaww8/zNChQ5WQiYik\nMJ3/J5LiHnnkET7++GPGjRtH/fr1Ex2OiIiUkJIykRT2r3/9i8GDBzNu3DgaNGiQ6HBERGQ/KCkT\nSVGPPfYY7777LpmZmRx00EGJDkdERPaTkjKRFPTEE0/w5ptvkpmZycEHH5zocEREJAoKGxJDRJLQ\nU089xRtvvMG4ceM45JBDEh2OiIhEiVrKRFLIs88+yyuvvEJmZiaNGjVKdDgiIhJFSspEUsTzzz/P\niy++SGZmJo0bN050OCIiEmVKykRSwAsvvMDzzz9PZmYmTZo0SXQ4IiISA0rKRJLcf//7X5555hnG\njRtH06ZNEx2OiIjEiJIykST2yiuv8OSTTzJu3DiaNWuW6HBERCSGlJSJJKnXXnuNxx57jHHjxtG8\nefNEhyMiIjEW9yExzKyJmY0zs7lm9oOZ3Rw8X8fMRpvZz2b2pZnVindsIsliwIAB/POf/2Ts2LG0\naNEi0eGIiEgcmHMuvgs0O/j/27v36KrKM4/j3weEFIYAyajcRKSAgjhCJWCQJIBKlVao0NF6GQvq\notYOmMq0q6hlFu3q2AHBKqDtQACBUsFiC4JdIIyGwEC4hosExaFFQaKoCRmCoCjP/HF2TpPIJVxy\n9jnJ77NW1jrn3bfn7BXI77z7ffcGWrr7VjNrAmwGbgfuBz529wlm9jMgxd3HVNnWY12vSCy5OzNn\nzmTcuHG8/vrrdOrUKeySRETkHJkZ7m7VXT/mly/d/QPgg+B1mZntAtoAg4G+wWqzgVxgzMn2IVJb\nlJWVsWnTJtatW0d+fj75+fk0bdqUlStXKpCJiNQxMe8pq3RwsyuAVcA1wHvunhK0G1Bc/r7C+uop\nk4Tl7rzzzjuVAtju3bvp1q0b6enppKen07t3by677DIi/wRERCSRxX1PWbng0uXLQLa7H674R8jd\n3cyUviShlZaWsmHDBvLz81m3bh3r168nOTk5GsCGDx9O9+7dSUpKCrtUERGJA6GEMjNrQCSQzXX3\nRUHzh2bW0t0/MLNWwMGTbTtu3Ljo6379+tGvX78arlbkzE6cOMGuXbuiASw/P5+9e/dy3XXX0bt3\nb0aMGEFOTo6eVSkiUovl5uaSm5t7ztuHMdDfiIwZ+8TdH63QPiFoG29mY4DmGugv8aq4uJj169dH\nA9iGDRu4+OKL6d27d7Qn7Nprr6VBgwZhlyoiIiE528uXYYSyDCAP2A6UH/wxYAPwEnA5sBe4090P\nVdlWoUxi7osvvmDnzp2VxoIdOHCAnj17RgNYeno6l1xySdiliohIHIn7UHY+FMokFg4ePBgNX/n5\n+WzatIk2bdpEB+Knp6fTtWtX6tevH3apIiISxxTKRM7C8ePH2bZtWzSArVu3jk8++YTrr78+GsB6\n9epFampq2KWKiEiCUSgTOY0DBw5UGoxfUFBA+/btK40F69y5M/XqxfxhFyIiUssolIkEPvvsMwoK\nCiqNBTty5Eile4L17NmTpk2bhl2qiIjUQgplUqe5O3l5eTz11FO88cYbXHXVVZXGgnXs2FE3ZhUR\nkZhImJvHilxIX375JYsWLWLChAkcOnSIn/zkJ8yfP58mTZqEXZqIiEi1KJRJQjt27Bhz5sxh4sSJ\npKamMmbMGAYPHqyZkSIiknAUyiQhlZSU8Nvf/pYpU6aQlpbGjBkzyMjI0KVJERFJWJpiJgll3759\njB49mg4dOrB7925WrFjBkiVLyMzMVCATEZGEplAmCeHNN99k2LBhdO/enXr16rF9+3ZeeOEFrrnm\nmrBLExERuSAUyiRuuTurVq3i29/+NgMGDKBLly7s2bOHiRMnctlll4VdnoiIyAWlMWUSd042k/Ll\nl1/ma1/7WtiliYiI1BiFMokbmkkpIiJ1mUKZhE4zKUVERDSmTEKkmZQiIiJ/p1AmMVd1JuW2bds0\nk1JEROo8PftSYqL8mZQTJkxgy5YtZGdn88Mf/pDmzZuHXZqIiFSgKxXn5mT5RM++lLhScSZlSUkJ\nP/3pTzWTUkQkzqkD5OxcqCCrUCY1QjMpRUREzo5CmVxQFWdS9ujRg5ycHA3cFxERqQYN9JcL4mQz\nKZcuXUpWVpYCmYiISDUolMl5KZ9J2a1bN8xMMylFRCRu1KtXj7/+9a8nXTZv3jxuueWWGFd0egpl\nctaqPpOyc+fO7Nmzh0mTJtG2bduwyxMRkVrq17/+Nd/61rcqtXXq1OmkbQsWLDjtvu69916WL18e\nfX+6ABcrGlMm1fbll1+yePFixo8fr5mUIiISc3379mX8+PG4O2ZGUVERX3zxBVu3buXEiRPUq1eP\noqIi9uzZQ1ZW1lnvP+xZp+opkzM6duwY06ZNo0uXLkyYMIExY8awa9cuRowYoUAmIiIxk5aWxvHj\nx9m6dSsAq1evpn///lx55ZWV2jp27EirVq0AWLFiBVdeeSUpKSmMHDkyuq8XXniBzMxMgGiA69at\nG8nJyfzxj38EYOnSpXTv3p2UlBT69OnDjh07avTzKZTJKX3yySc8+eSTtG/fnldeeYWcnBzWrVvH\nkCFDdGsLERGJuYYNG3L99dezatUqAPLy8sjMzCQjI4O8vLxoW8VesldffZVNmzaxfft2XnrppUqX\nLMuVb7t9+3YOHz7MHXfcQUFBAQ8++CDTp0+nuLiYhx56iMGDB/P555/X2OdTKJOv2L59OyNGjKBj\nx4688847mkkpIiJxo2/fvtEQtWbNGrKyssjMzIy2rV69mr59+0bXHzNmDE2bNqVt27b0798/2qN2\nJtOmTeOhhx6iZ8+emBnf//73SUpKIj8//8J/qIBCmQB/v/N+//79GThwIO3atePtt99m1qxZmkkp\nIiKVmJ3/z7nKyspizZo1lJSU8NFHH9GhQwd69+7N2rVrKSkpYefOnZV6ylq2bBl93bhxY44cOVKt\n47z77rtMmjSJlJSU6M/+/fspKio69+LPQAP967iSkhJmzJjBc889R8uWLcnOzmbo0KE0bNgw7NJE\nRCROhTkePj09ndLSUqZPn06fPn0AaNq0Ka1bt2batGm0bt2adu3anfdxLr/8cp544gkef/zx895X\ndamnrI4qLCzk4Ycf5utf/zrbtm1jwYIFrFu3jrvuukuBTERE4lajRo1IS0vj6aefrtQjlpGRwdNP\nP13p0mVV7n7KGZYtWrRgz5490fcjRozgd7/7HRs2bMDdOXLkCK+++iplZWUX7sNUoVBWh5w4cYKl\nS5fyzW9+k5tuuokWLVpQWFjI3Llz6dWrV9jliYiIVEvfvn356KOPyMjIiLZlZmby8ccfVwpqVcdB\nm1m0reJrgHHjxjFs2DBSUlJYuHAhPXr0YPr06YwcOZLU1FQ6derEnDlzavRzWdj35DgbZuaJVG+8\nKC0tZdasWUydOpWUlBSys7O54447SEpKCrs0ERGJM2YW+v26Es2pzlnQXu0RdBpTVou9/fbbTJ06\nNfooiblz55Kenq4ZlCIiInFIoayWOXHiBK+99hrPPvssmzdv5gc/+AE7duygTZs2YZcmIiIip6FQ\nVkscPnyY2bNnM2XKFBo1akR2djZ//vOfdcd9ERGRBKFQluD27NnD1KlTmTNnDv379ycnJ4eMjAxd\nohQREUkwmn2ZgNydlStXMnjwYNLT00lKSqKgoICFCxeSmZmpQCYiIpKA1FOWQI4cOcLvf/97Jk+e\nTL169XjkkUeYP38+jRs3Drs0EREROU8KZQlg7969PPfcc8yaNYuMjAymTp1Kv3791CMmIiJSi+jy\nZZxyd3Jzcxk6dChpaWm4Oxs3bow+n1KBTEREpHZRKIszR48eZcaMGXTv3p2HH36YAQMGsHfvXiZO\nnEj79u3DLk9ERCQuDR8+nLFjx57XPnJzc2nbtu0FqujsKZTFiX379vHYY4/Rrl07Fi1axMSJE6PP\np2zSpEnY5YmIiITuiiuuoHHjxiQnJ5Oamsptt93G/v37ga8+NikRKZSFyN1Zs2YNd955J927d+fo\n0aOsXbuWJUuWMGDAgIT/5RIREbmQzIylS5dy+PBhioqKaNGiBaNGjYouT/THQymUheDYsWPMnj2b\ntLQ0HnjgATIzM9m7dy/PPPMMHTt2DLs8ERGRuJeUlMR3v/tdCgsLgcqBrKSkhNtuu41LL72U1NRU\nBg0axPvvvx9dXlxczP3330+bNm1ITU1lyJAhJz3G5MmT6dq1KwcOHKjZDxNQKIuhAwcOMHbsWNq1\na8eLL77Ir371K9566y1GjRpFcnJy2OWJiIjEvfLw9emnn7JgwQJ69+4NUOnqkrvz4IMP8t577/He\ne+/RqFEjRo4cGV1+3333cezYMQoLCzl48CCjR4/+ynF++ctfMmfOHPLy8mjdunUNf6oI3RIjBrZs\n2cLEiRNZtmwZ99xzD6tWraJz585hlyUiIpJQ3J3bb7+diy66iCNHjnDppZeybNmyr6xXtffr8ccf\n58YbbwSgqKiIZcuWUVxcTLNmzQDIzMysdIzRo0ezadMm3njjjZh2miiUxcDOnTvp2bMnzz//PM2b\nNw+7HBERkfNyIcY8n8v4LzNj8eLF3Hjjjbg7ixYtom/fvtFLmOU+/fRTHn30UZYvX05JSQkAZWVl\nuDv79u0jNTU1GsiqOnToEDk5OcyfPz/mV7F0+TIG7rvvPh599FEFMhERqRXc/bx/zpeZMWTIEOrX\nr8+aNWuibQCTJk1i9+7dbNiwgdLSUlatWhU9btu2bSkuLqa0tPSk+01JSWHp0qXcf//9rF279rzr\nPBsKZSIiIpIwygOdu7N48WIOHTrE1VdfXSnslZWV0ahRI5o1a0ZxcTG/+MUvotu3atWKgQMH8qMf\n/YhDhw5x/Phx8vLyKh0jKyuLefPmMXToUDZu3Bizz6ZQJiIiIglj0KBBJCcn06xZM8aOHcvs2bPp\n0qVLpfuU/fjHP+bo0aNcfPHF3HDDDQwcOLDSJde5c+fSoEEDOnfuTIsWLZg8eXJ0Wfl6N998MzNn\nzmTQoEFs3bo1Jp/NEumeHmbmiVSviIhIojGzhL/fV6yd6pwF7dUegKeeMhEREZE4oFAmIiIiEgcU\nykRERETigEKZiIiISBxQKBMRERGJAwplIiIiInFAoUxEREQkDujZlyIiIlLJhXi2pZy9uAplZnYr\n8AxQH8hx9/EhlyQiIlKn6Max4Ymby5dmVh+YCtwKXA3cbWZdwq1KcnNzwy6hztE5jz2d89jTOY89\nnfP4FzehDOgF/K+773X348B84Dsh11Tn6R9x7Omcx57OeezpnMeeznn8i6dQ1gbYV+H9/qBNRERE\npNaLp1Cmi9giIiJSZ1m8DOgzs3RgnLvfGrx/DDhRcbC/mcVHsSIiIiLV4O7VnsoaT6HsIuBt4Cbg\nALABuNvdd4VamIiIiEgMxM0tMdz9CzMbCSwnckuMGQpkIiIiUlfETU+ZiIiISF0WTwP9T8vMbjWz\nt8zsHTP7Wdj11HZm1tbM3jCznWb2ppk9EnZNdYWZ1TezAjNbEnYtdYGZNTezhWa2y8wKg/GtUoPM\n7LHg/5YdZvYHM0sKu6baxsxmmtmHZrajQluqma0ws91m9pqZNQ+zxtrmFOf8qeD/lm1m9icza3a6\nfSREKNONZUNxHHjU3bsC6cC/6pzHTDZQiGYkx8qzwF/cvQtwLaBhEzXIzK4ARgDXufs/ERmucleY\nNdVSs4j8zaxoDLDC3a8E/jt4LxfOyc75a0BXd+8G7AYeO90OEiKUoRvLxpy7f+DuW4PXZUT+ULUO\nt6raz8wuA74F5AB6+FwNC761Zrr7TIiMbXX30pDLqu3+j8iXvsbBBK/GwPvhllT7uPtqoKRK82Bg\ndvB6NnB7TIuq5U52zt19hbufCN6uBy473T4SJZTpxrIhCr7ZfoPIL5TUrN8APwVOnGlFuSDaAx+Z\n2Swz22Jm082scdhF1WbuXgxMAt4jMtP+kLuvDLeqOqOFu38YvP4QaBFmMXXQA8BfTrdCooQyXcYJ\niZk1ARYC2UGPmdQQM7sNOOjuBaiXLFYuAq4Dnnf364Aj6JJOjTKzDsCPgSuI9L43MbN7Qy2qDvLI\nLD/9bY0RM3sC+Nzd/3C69RIllL0PtK3wvi2R3jKpQWbWAHgZ+L27Lwq7njrgBmCwmf0NeBG40czm\nhFxTbbcf2O/uG4P3C4mENKk5acBad//E3b8A/kTkd19q3odm1hLAzFoBB0Oup04ws+FEhqWc8ctH\nooSyTUAnM7vCzBoC3wNeCbmmWs3MDJgBFLr7M2HXUxe4++Pu3tbd2xMZ+Py6u38/7LpqM3f/ANhn\nZlcGTTcDO0MsqS54C0g3s0bB/zM3E5nYIjXvFWBY8HoYoC/bNczMbiUyJOU77n7sTOsnRCgLvk2V\n31i2EFigG8vWuD7AvwD9g9szFAS/XBI7urQQG6OAeWa2jcjsyydDrqdWc/dtwBwiX7a3B83Twquo\ndjKzF4G1wFVmts/M7gf+ExhgZruBG4P3coGc5Jw/AEwBmgArgr+jz592H7p5rIiIiEj4EqKnTERE\nRKS2UygTERERiQMKZSIiIiJxQKFMREREJA4olImIiIjEAYUyERERkTigUCYi58TMWprZfDP7XzPb\nZGavmlmnYFlXM3vdzN4ys91m9vMK2w03sxNmdlOFttuDtqHB+9xg261mtqbCzV0xs0Vmtq5KLVcF\n2xSYWaGZ/VfNn4FzE9TZ4xy262Fmzwavh5vZlAtfnYiESaFMRM5acCf2PxN56kBHd08DHgNamFkj\nYDHwpLt3BroBN5jZjyrsYgeRpxaUuxvYWuG9A/e4e3dgNvBUcNzmwDVAQzNrX2H9ycAkd/+Gu19N\n5IaNNcrMLjrHTc/pmYPuvtndsyvsQ0RqGYUyETkX/Yk8XDd6J3Z33+7ua4B7gDXuvjJoP0rkiRzl\nD/p2YDXQy8wuCh563wHYdopjrQY6Bq+HAkuAP1I51LUk8ozc8lrerLoTM2tlZnlBb9oOM+sTtN9q\nZpuDXrmVQVtq0CO3zczWmdk/Be3jzGyuma0BZpvZxWa20Mw2BD9feYZj8Dih+UEP3p+ARhWWfdPM\n1gbHf8nM/iFo72lm/xPUtN7MmphZPzNbUr5phX1ccqYaRCQxnOs3PRGp264BNp9i2dVVl7n7X4Ng\nkVzeBKwAbgGaEXkmX3sqKw8eg/j743juAv6dyIOUFwG/Dtp/A7xuZmuB14BZ7l5aZX93A8vc/Ukz\nqwc0NrNLiDziJ9Pd3w164gB+AWx299vNrD+RxwJ9I1jWGchw98/M7A/Ab9z9f8zscmBZ8Pkrehgo\nc/erg3C3BcDMLgaeAG5y96Nm9jNgtJn9J7AAuMPdNweh9WjVk1zBs9WoQUQSgEKZiJyLM10+s1O0\ne4VlC4BsoCnwb8DjVbafZ2ZHgb8Bo8ysBdDR3fMBzOxzM+vq7jvd/QUzWw7cCnwHeMjMurn75xX2\nuRGYaWYNgEXuvi0IXKvc/V0Adz8UrNuHSK8c7v6Gmf1jECgdeMXdPwvWuxnoErmaC0CymTV2908r\nHDeTSHDC3XeYWXnATCcSntYG2zckeG4ecMDdNwfblAWf9xSntFo1iEgCUCgTkXOxE/jnUywrBLIq\nNpjZ14n0FpWVhwd332hm1wBH3P2dKqGjfEzZlgr7GAWkmtnfgqZkIr1fPw/2VwTMAmaZ2Q6gK1AQ\n3aH7ajPLBG4DXjCzp4ESTh0gT9X+aZV1rq8S/qqzr/L3K9z9nkoLgkulZ6G6NYhInNOYMhE5a+7+\nOpBkZiPK28zsWjPLAOYBGeWzK4OB/5OB8SfZ1Rgq95BVVDXI3A3c4u7t3b09kEYwriwYF9YgeN0S\n+EcqjDEL2i8HPnL3HCCHyOXIfCDLzK4I1kkNVl8N3Bu09Qu2O3ySml4DHqlwjO4n+Rx5RMbZEYTQ\na4mEznygj5l1CJb9g0Vmr74FtDKztKA92czqn+IcVbcGEUkACmUicq6GADdb5JYYbwL/ARS5+zEi\nlxB/bmZvERkPtt7dnwu2i84+dPdl7r7qFPuPXiINQlNbd18fXei+FzhkZr2AAcAOM9tKZEzVT9z9\nYJX99QO2mtkW4E7gWXf/GPgB8Kdg2xeDdccBPcxsG/AkMKxq7YFHgLRgQsDOYF9V/RZoYmaFRMaq\nbQrq/xgYDrwYHGctcJW7Hwe+B0wJaloOfK3KsSu+rk4NIpIAzF0zq0VERETCpp4yERERkTigUCYi\nIiISBxTKREREROKAQpmIiIhIHFAoExEREYkDCmUiIiIicUChTERERCQOKJSJiIiIxIH/B30xZvwZ\niDtqAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x108c0d710>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"X = np.arange(1,11)\n",
"X2 = np.array([1,1,1,1,1,0,0,0,0,0,])\n",
"plt.plot(X, 100/(1+np.exp(.5*(3-X+np.random.uniform(size=10)))), color='blue')\n",
"plt.plot(X, 100/(1+np.exp(.5*(3-X+3*X2+np.random.uniform(size=10)))), color='black')\n",
"plt.title(\"Hypothetical conditional effect plot, biased against low-risk black offenders\")\n",
"plt.ylabel('Probability of two-year recidivism')\n",
"plt.xlabel('COMPAS score decile')\n",
"plt.xlim(0,12)\n",
"plt.ylim(0,100)\n",
"plt.legend([\"White\", \"Black\"], loc='lower right')"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x109fcca10>"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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WT0nZGcBA4ENgazTY3f3WxIaWbSwqKRMp4wYNgn/8I5SQHa1GFCK5WrduHePG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\nnz+ZwzGZTmhmcgXvV0CrSsu+KmlFcvxnJR2XtF8g6V+Tml6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"text/plain": [
"<matplotlib.figure.Figure at 0x109bc85d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"X = np.arange(1,11)\n",
"X2 = np.array([0,0,0,0,.2,.4,.6,.8,1,1.2,])\n",
"plt.plot(X, 100/(1+np.exp(.5*(3-X+np.random.uniform(size=10)))), color='blue')\n",
"plt.plot(X, 100/(1+np.exp(.5*(3-X+4*X2+np.random.uniform(size=10)))), color='black')\n",
"plt.title(\"Hypothetical conditional effects plot, biased against high-risk black offenders\")\n",
"plt.ylabel('Probability of two-year recidivism')\n",
"plt.xlabel('COMPAS score decile')\n",
"plt.xlim(0,12)\n",
"plt.ylim(0,100)\n",
"plt.legend([\"White\", \"Black\"], loc='lower right')"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
@thejefflarson
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thejefflarson commented Aug 6, 2016

Hi Abe,

Thanks for taking a look at our analysis. Unfortunately, you’ve misinterpreted how the test works. You are treating the decile scores as independent factors when they are ordinal. What this means in practice is that a user of the score is going to choose a threshold and defendants who score higher than that threshold will have more consequences from criminal justice agencies. In fact, COMPAS’s own guide says that defendants with scores higher than low “garner more interest from supervision agencies.”

In fact in medicine, when analyzing prognostic models like this, interpreting these thresholds as cutoffs in this way is standard practice.

Northpointe agrees with this interpretation, as you can see in the tradeoff tables they published in their report:

screen shot 2016-08-06 at 1 09 13 pm
screen shot 2016-08-06 at 1 09 02 pm

As you can see the difference the false positive rate across thresholds greater than or equal to 5 (Northpointe's cutoff) is higher for black defendants.

In other words, the score over predicts black defendant’s risk of recidivism. What that means in practice is that in Broward county, 805 black defendants were labelled as a higher risk of recidivating when they didn’t.

You also might be interested to read our technical response to Northpointe’s critique, which includes a cox regression model that controls for differences in age, sex, and criminal history across these groups and confirms our findings.

One other note, you’ve made mistake in the fields you chose to analyze. You’ve taken a two year sample of general recidivism — “compas-two-years.csv” — and used the violent recidivism score v_decile_score. You should have used the decile_score column for general recidivism. If you wanted to analyze the violent recidivism score you should have used the “compas-two-years-violent.csv” dataset instead.

I hope you are enjoying this great summer weekend!

--Jeff

@abegong
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abegong commented Aug 7, 2016

Jeff -
It's great to hear from you! Thanks for the thoughtful response—and for putting this issue on the map in such a powerful way. I’m a huge fan of the way you’re combining data science with journalism.

Thanks also for setting me straight on the v_decile_score issue. The conditional means plot tightens up quite a bit after matching to the non-violent decile score with the non-violent outcomes data:

image

The thing I’m hung up on is the difference between statistical bias (as in E[Y] ≠ Ŷ) and rates of false positives. As I read it, the strongest criticisms you’ve leveled against COMPAS are expressed in terms of sensitivity and specificity. Those are worth considering, but they’re neither necessary nor sufficient to conclude that the algorithm is biased.

For clarity: are you claiming that the algorithm is statistically biased against blacks? Or are you saying that it's unfair, even if it’s not biased in a statistical sense?

Cheers from the west coast,
-- Abe

@thejefflarson
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Hi Abe,

You should take a closer look at our published articles.

Enjoy your Sunday!

--Jeff

@abegong
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abegong commented Aug 7, 2016

Jeff -
I've read each of your articles about COMPAS carefully, more than once. I've also worked my way through NorthPointe's (very dense) response.

My read is that you're talking past each other. You're throwing around the word "bias," but your stats only address false positives. NorthPointe is arguing that there's no statistical bias, with supporting evidence.

When they include tables that show how the numbers play out in terms of false positives, you're picking them up and saying "See? Bias." (Your initial response above is a perfect example.) But that's not bias under the statistical definition.

That's why I'm asking the question: in all your writing, you've never once defined exactly what you mean by "bias."

If you're claiming that COMPAS is statistically biased against blacks, then your evidence needs to support that claim. From what I've seen, the evidence isn't there. But I'm willing to be proven wrong if you have proof you haven't brought forward yet.

If you're using "bias" in a less precise, non-statistical way, that's okay too. (I don't love the conflation of statistical terminology, but I can see why you'd go there.) At this point, you should just clear it up, so that the thousands of confused data scientists following this issue can get on with their lives.

What do you say? Will you clarify your terminology, so the conversation can move forward?

I'm not trying to be a jerk here---just looking for clarity in an important public debate.

Hope you enjoy your Sunday, too!
-- Abe

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