lectures/22_ethics.ipynb

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   "source": [
    "![](img/330-banner.png)"
   ]
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   },
   "source": [
    "# Lecture 22: Ethics\n",
    "\n",
    "UBC 2022-23\n",
    "\n",
    "Instructor: Varada Kolhatkar"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b36d7f78-2d71-4dd0-8745-ae81795e9e2b",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "## Imports"
   ]
  },
  {
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   "execution_count": 1,
   "id": "4e75fe86-cffe-4014-91e9-b1f6143af74b",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "\n",
    "import IPython\n",
    "import matplotlib.pyplot as plt\n",
    "import mglearn\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.dummy import DummyClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score\n",
    "from sklearn.model_selection import cross_val_score, cross_validate, train_test_split\n",
    "from sklearn.pipeline import Pipeline, make_pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "from sklearn.metrics import ConfusionMatrixDisplay  # Recommended method in sklearn 1.0\n",
    "\n",
    "%matplotlib inline\n",
    "pd.set_option(\"display.max_colwidth\", 200)\n",
    "\n",
    "from IPython.display import Image"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c05fe12-8850-41b1-ae77-f0207486d903",
   "metadata": {},
   "source": [
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d498cd53-411d-4b56-9ed2-274e47e9f98b",
   "metadata": {},
   "source": [
    "## Lecture plan \n",
    "\n",
    "- Guest lecture by [Giulia Toti](https://www.gtoti.com/) (~50 mins)\n",
    "    - The slides are available [here](https://github.com/UBC-CS/cpsc330/blob/master/lectures/22_ethics-expanded.pptx).\n",
    "- ML fairness activity (~15 mins)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c1ede64-9816-4642-8815-8bf1da81fa23",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## ML fairness activity"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b497bb81-5739-4b68-95d3-b7bf007a9346",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "AI/ML systems can give the illusion of objectivity as they are derived from seemingly unbiased data & algorithm. However, human are inherently biased and AI/ML systems, if not carefully evaluated, can even further amplify the existing inequities and systemic bias in our society.  \n",
    "\n",
    "How do we make sure our AI/ML systems are *fair*? Which metrics can we use to quatify 'fairness' in AI/ML systems?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61ba95e4-cd34-4820-af69-2a062a22453a",
   "metadata": {},
   "source": [
    "Let's examine this on [the adult census data set](https://www.kaggle.com/uciml/adult-census-income). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "05750691-79b9-4e90-b32a-68b17a5e77f7",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32561, 15)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "census_df = pd.read_csv(\"data/adult.csv\")\n",
    "census_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f27562ae-3e28-47c1-82d6-697589990f9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df, test_df = train_test_split(census_df, test_size=0.4, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "088c42cc-2a97-4c62-b0ff-2d785abb0168",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education.num</th>\n",
       "      <th>marital.status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital.gain</th>\n",
       "      <th>capital.loss</th>\n",
       "      <th>hours.per.week</th>\n",
       "      <th>native.country</th>\n",
       "      <th>income</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25823</th>\n",
       "      <td>36</td>\n",
       "      <td>Private</td>\n",
       "      <td>245521</td>\n",
       "      <td>7th-8th</td>\n",
       "      <td>4</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Farming-fishing</td>\n",
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       "      <td>&lt;=50K</td>\n",
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       "      <th>10274</th>\n",
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       "      <td>Assoc-voc</td>\n",
       "      <td>11</td>\n",
       "      <td>Never-married</td>\n",
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       "      <td>Own-child</td>\n",
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       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27652</th>\n",
       "      <td>25</td>\n",
       "      <td>Local-gov</td>\n",
       "      <td>109526</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Craft-repair</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
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       "    <tr>\n",
       "      <th>13941</th>\n",
       "      <td>23</td>\n",
       "      <td>Private</td>\n",
       "      <td>131275</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Craft-repair</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>Amer-Indian-Eskimo</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31384</th>\n",
       "      <td>27</td>\n",
       "      <td>Private</td>\n",
       "      <td>193122</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
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       "      <th>29802</th>\n",
       "      <td>25</td>\n",
       "      <td>Private</td>\n",
       "      <td>410240</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Craft-repair</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5390</th>\n",
       "      <td>51</td>\n",
       "      <td>Private</td>\n",
       "      <td>146767</td>\n",
       "      <td>Assoc-voc</td>\n",
       "      <td>11</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>United-States</td>\n",
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       "      <th>860</th>\n",
       "      <td>55</td>\n",
       "      <td>Federal-gov</td>\n",
       "      <td>238192</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Tech-support</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>1887</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15795</th>\n",
       "      <td>41</td>\n",
       "      <td>Private</td>\n",
       "      <td>154076</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23654</th>\n",
       "      <td>22</td>\n",
       "      <td>Private</td>\n",
       "      <td>162667</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19536 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age    workclass  fnlwgt     education  education.num  \\\n",
       "25823   36      Private  245521       7th-8th              4   \n",
       "10274   26      Private  134287     Assoc-voc             11   \n",
       "27652   25    Local-gov  109526       HS-grad              9   \n",
       "13941   23      Private  131275       HS-grad              9   \n",
       "31384   27      Private  193122       HS-grad              9   \n",
       "...    ...          ...     ...           ...            ...   \n",
       "29802   25      Private  410240       HS-grad              9   \n",
       "5390    51      Private  146767     Assoc-voc             11   \n",
       "860     55  Federal-gov  238192       HS-grad              9   \n",
       "15795   41      Private  154076  Some-college             10   \n",
       "23654   22      Private  162667       HS-grad              9   \n",
       "\n",
       "           marital.status         occupation relationship                race  \\\n",
       "25823  Married-civ-spouse    Farming-fishing      Husband               White   \n",
       "10274       Never-married              Sales    Own-child               White   \n",
       "27652  Married-civ-spouse       Craft-repair      Husband               White   \n",
       "13941       Never-married       Craft-repair    Own-child  Amer-Indian-Eskimo   \n",
       "31384  Married-civ-spouse  Machine-op-inspct      Husband               White   \n",
       "...                   ...                ...          ...                 ...   \n",
       "29802       Never-married       Craft-repair    Own-child               White   \n",
       "5390   Married-civ-spouse     Prof-specialty      Husband               White   \n",
       "860    Married-civ-spouse       Tech-support      Husband               White   \n",
       "15795  Married-civ-spouse       Adm-clerical      Husband               White   \n",
       "23654       Never-married  Handlers-cleaners    Own-child               White   \n",
       "\n",
       "          sex  capital.gain  capital.loss  hours.per.week native.country  \\\n",
       "25823    Male             0             0              35         Mexico   \n",
       "10274  Female             0             0              35  United-States   \n",
       "27652    Male             0             0              38  United-States   \n",
       "13941    Male             0             0              40  United-States   \n",
       "31384    Male             0             0              40  United-States   \n",
       "...       ...           ...           ...             ...            ...   \n",
       "29802    Male             0             0              40  United-States   \n",
       "5390     Male             0             0              40  United-States   \n",
       "860      Male             0          1887              40  United-States   \n",
       "15795    Male             0             0              50  United-States   \n",
       "23654    Male             0             0              50       Portugal   \n",
       "\n",
       "      income  \n",
       "25823  <=50K  \n",
       "10274  <=50K  \n",
       "27652  <=50K  \n",
       "13941  <=50K  \n",
       "31384  <=50K  \n",
       "...      ...  \n",
       "29802  <=50K  \n",
       "5390    >50K  \n",
       "860     >50K  \n",
       "15795   >50K  \n",
       "23654  <=50K  \n",
       "\n",
       "[19536 rows x 15 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f2021c6c-8b00-4f94-9697-6b544f66b1b3",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(19536, 15)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df_nan = train_df.replace(\"?\", np.nan)\n",
    "test_df_nan = test_df.replace(\"?\", np.nan)\n",
    "train_df_nan.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8991325a-c826-4937-9b26-0ab1dffcb6cb",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "# Let's identify numeric and categorical features\n",
    "\n",
    "numeric_features = [\n",
    "    \"age\",\n",
    "    \"capital.gain\",\n",
    "    \"capital.loss\",\n",
    "    \"hours.per.week\",\n",
    "]\n",
    "\n",
    "categorical_features = [\n",
    "    \"workclass\",\n",
    "    \"marital.status\",\n",
    "    \"occupation\",\n",
    "    \"relationship\",\n",
    "    \"race\",\n",
    "    \"native.country\",\n",
    "]\n",
    "\n",
    "ordinal_features = [\"education\"]\n",
    "binary_features = [\n",
    "    \"sex\"\n",
    "]  # Not binary in general but in this particular dataset it seems to have only two possible values\n",
    "drop_features = [\"education.num\", \"fnlwgt\"]\n",
    "target = \"income\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "01e7bd33-d03b-4d06-96f6-30bbb3044623",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['7th-8th', 'Assoc-voc', 'HS-grad', 'Bachelors', 'Some-college',\n",
       "       '10th', '11th', 'Prof-school', '12th', '5th-6th', 'Masters',\n",
       "       'Assoc-acdm', '9th', 'Doctorate', '1st-4th', 'Preschool'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[\"education\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7abfc75b-a026-4c93-88e0-974b0db48fc2",
   "metadata": {},
   "outputs": [],
   "source": [
    "education_levels = [\n",
    "    \"Preschool\",\n",
    "    \"1st-4th\",\n",
    "    \"5th-6th\",\n",
    "    \"7th-8th\",\n",
    "    \"9th\",\n",
    "    \"10th\",\n",
    "    \"11th\",\n",
    "    \"12th\",\n",
    "    \"HS-grad\",\n",
    "    \"Prof-school\",\n",
    "    \"Assoc-voc\",\n",
    "    \"Assoc-acdm\",\n",
    "    \"Some-college\",\n",
    "    \"Bachelors\",\n",
    "    \"Masters\",\n",
    "    \"Doctorate\",\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "13387510-f6fd-4cb6-8016-54e3cdbd4d41",
   "metadata": {},
   "outputs": [],
   "source": [
    "assert set(education_levels) == set(train_df[\"education\"].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e3c41aaa-a0b3-48a1-9c30-354defec4be0",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "X_train = train_df_nan.drop(columns=[target])\n",
    "y_train = train_df_nan[target]\n",
    "\n",
    "X_test = test_df_nan.drop(columns=[target])\n",
    "y_test = test_df_nan[target]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7a0d9dd1-69b8-427a-8ea0-0990bc685958",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.compose import ColumnTransformer, make_column_transformer\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import OneHotEncoder, OrdinalEncoder, StandardScaler\n",
    "\n",
    "numeric_transformer = make_pipeline(StandardScaler())\n",
    "\n",
    "ordinal_transformer = OrdinalEncoder(categories=[education_levels], dtype=int)\n",
    "\n",
    "categorical_transformer = make_pipeline(\n",
    "    SimpleImputer(strategy=\"constant\", fill_value=\"missing\"),\n",
    "    OneHotEncoder(handle_unknown=\"ignore\", sparse=False),\n",
    ")\n",
    "\n",
    "binary_transformer = make_pipeline(\n",
    "    SimpleImputer(strategy=\"constant\", fill_value=\"missing\"),\n",
    "    OneHotEncoder(drop=\"if_binary\", dtype=int),\n",
    ")\n",
    "\n",
    "preprocessor = make_column_transformer(\n",
    "    (numeric_transformer, numeric_features),\n",
    "    (ordinal_transformer, ordinal_features),\n",
    "    (binary_transformer, binary_features),\n",
    "    (categorical_transformer, categorical_features),\n",
    "    (\"drop\", drop_features),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "709290c2-4ae3-40de-8f00-8bd346b93743",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<=50K    14841\n",
       ">50K      4695\n",
       "Name: income, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "800237aa-e64f-4dbe-b6d1-b91c5a9c984c",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "pipe_lr = make_pipeline(\n",
    "    preprocessor, LogisticRegression(class_weight=\"balanced\", max_iter=1000)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e25c1218-ef32-4995-a8d7-b2fe42a95c5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe_lr.fit(X_train, y_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c12230eb-bb7e-4964-bf0e-0c9bb3291e33",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = ConfusionMatrixDisplay.from_estimator(pipe_lr, X_test, y_test);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd4524b9-f837-488e-b929-d1f01bf302f0",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Let's examine confusion matrix separately for the two genders we have in the data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "06c88c01-68b3-4db0-b3ce-ec368bd5b64c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['x0_Male'], dtype=object)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_enc = preprocessor.fit_transform(X_train)\n",
    "preprocessor.named_transformers_[\"pipeline-2\"][\"onehotencoder\"].get_feature_names_out()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b3bce4e1-465f-4b59-ae9f-2d05c3f36a4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education</th>\n",
       "      <th>education.num</th>\n",
       "      <th>marital.status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital.gain</th>\n",
       "      <th>capital.loss</th>\n",
       "      <th>hours.per.week</th>\n",
       "      <th>native.country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14160</th>\n",
       "      <td>29</td>\n",
       "      <td>Private</td>\n",
       "      <td>280618</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27048</th>\n",
       "      <td>19</td>\n",
       "      <td>Private</td>\n",
       "      <td>439779</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28868</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>204734</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Tech-support</td>\n",
       "      <td>Wife</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5667</th>\n",
       "      <td>35</td>\n",
       "      <td>Private</td>\n",
       "      <td>107991</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>United-States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7827</th>\n",
       "      <td>20</td>\n",
       "      <td>Private</td>\n",
       "      <td>54152</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       age workclass  fnlwgt     education  education.num      marital.status  \\\n",
       "14160   29   Private  280618  Some-college             10  Married-civ-spouse   \n",
       "27048   19   Private  439779  Some-college             10       Never-married   \n",
       "28868   28   Private  204734  Some-college             10  Married-civ-spouse   \n",
       "5667    35   Private  107991          11th              7       Never-married   \n",
       "7827    20   Private   54152  Some-college             10       Never-married   \n",
       "\n",
       "              occupation   relationship   race     sex  capital.gain  \\\n",
       "14160  Handlers-cleaners        Husband  White    Male             0   \n",
       "27048              Sales      Own-child  White    Male             0   \n",
       "28868       Tech-support           Wife  White  Female             0   \n",
       "5667               Sales  Not-in-family  White    Male             0   \n",
       "7827        Adm-clerical      Own-child  White  Female             0   \n",
       "\n",
       "       capital.loss  hours.per.week native.country  \n",
       "14160             0              40  United-States  \n",
       "27048             0              15  United-States  \n",
       "28868             0              40  United-States  \n",
       "5667              0              45  United-States  \n",
       "7827              0              30            NaN  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8c5ea7d3-78c1-467e-8557-9b339e9f8413",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "X_female = X_test.query(\"sex=='Female'\")  # X where sex is female\n",
    "X_male = X_test.query(\"sex=='Male'\")  # X where sex is male\n",
    "\n",
    "y_female = y_test[X_female.index]  # y where sex is female\n",
    "y_male = y_test[X_male.index]  # y where sex is male"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "542f393a-7045-45bc-a205-95606c30e2ca",
   "metadata": {},
   "source": [
    "**Get predictions for `X_female` and `y_male` with `pipe_lr`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "09505b4f-c3f3-4f8b-98e8-88d94492792f",
   "metadata": {},
   "outputs": [],
   "source": [
    "female_preds = pipe_lr.predict(X_female)\n",
    "male_preds = pipe_lr.predict(X_male)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a131f0c-d47f-463d-8ecf-3719dc883271",
   "metadata": {},
   "source": [
    "Let's examine the accuracy and confusion matrix for female class.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "7e577d03-68fd-4fe1-89e3-c84ee3ebf09f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize=(10, 5))\n",
    "ConfusionMatrixDisplay.from_estimator(pipe_lr, X_female, y_female, ax = ax[0], normalize=\"true\");\n",
    "ax[0].set_title('Female confusion matrix')\n",
    "ax[1].set_title('Male confusion matrix')\n",
    "ConfusionMatrixDisplay.from_estimator(pipe_lr, X_male, y_male, ax = ax[1], normalize=\"true\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "175fe75e-6998-48ba-a31b-edc182e7b0d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "       <=50K       0.96      0.94      0.95      3851\n",
      "        >50K       0.57      0.66      0.61       463\n",
      "\n",
      "    accuracy                           0.91      4314\n",
      "   macro avg       0.76      0.80      0.78      4314\n",
      "weighted avg       0.92      0.91      0.91      4314\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_female, female_preds))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "811eac63-221d-45e3-8db8-71514b6562a9",
   "metadata": {},
   "source": [
    "Let's examine the accuracy and confusion matrix for male class.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4eab7a1a-5a56-485c-a078-e662699c8ed5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "       <=50K       0.91      0.72      0.80      6028\n",
      "        >50K       0.57      0.85      0.68      2683\n",
      "\n",
      "    accuracy                           0.76      8711\n",
      "   macro avg       0.74      0.78      0.74      8711\n",
      "weighted avg       0.81      0.76      0.77      8711\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_male, male_preds))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "450d938b-c61f-46b5-b3cc-13a2d70f2a5e",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "### ❓❓ Questions for group discussion\n",
    "\n",
    "Let's assume that a company is using this classifier for loan approval with a simple rule that if the income is >=50K, approve the loan, else reject the loan. \n",
    "\n",
    "Discuss the questions below with your neighbour(s) and write the main points from your discussion in this [Google document](https://docs.google.com/document/d/1BJEpMm6luVOTcbC1Qth74jP40S1jErXumR6KbhnJeZM/edit#). \n",
    "\n",
    "1. Which group has a higher accuracy?\n",
    "2. Which group has a higher precision for class >50K? What about recall for class >50K?\n",
    "3. Will both groups have more or less the same proportion of people with approved loans? \n",
    "4. If a male and a female have both a certain level of income, will they have the same chance of getting the loan?\n",
    "5. Banks want to avoid approving unqualified applications (false positives) because default loan could have detrimental effects for them. Compare the false positive rates for the two groups.    \n",
    "6. Overall, do you think this income classifier will fairly treat both groups? What will be the consequences of using this classifier in loan approval application? \n"
   ]
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    "**Time permitting**\n",
    "1. Do you think the effect will still exist if the sex feature is removed from the model (but you still have it available separately to do the two confusion matrices)? \n",
    "2. Are there any other groups in this dataset worth examining for biases? "
   ]
  }
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