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"metadata": {
"colab": {
"name": "intro_to_fairness.ipynb",
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"cells": [
{
"cell_type": "markdown",
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"colab_type": "text"
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"source": [
"<a href=\"https://colab.research.google.com/gist/johnleung8888/35f794861b2df4b9556b529321887cd6/intro_to_fairness.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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},
{
"cell_type": "code",
"metadata": {
"cellView": "form",
"id": "y5T8lbpLd1sr"
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"source": [
"#@title Copyright 2020 Google LLC. Double-click here for license information.\n",
"# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
"# you may not use this file except in compliance with the License.\n",
"# You may obtain a copy of the License at\n",
"#\n",
"# https://www.apache.org/licenses/LICENSE-2.0\n",
"#\n",
"# Unless required by applicable law or agreed to in writing, software\n",
"# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
"# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
"# See the License for the specific language governing permissions and\n",
"# limitations under the License."
],
"execution_count": 1,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "84x4Fxc5lzFv"
},
"source": [
"# Introduction to Fairness in ML\n",
"***"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "J8daw3YOIAXH"
},
"source": [
"## Disclaimer\n",
"This exercise explores just a small subset of ideas and techniques relevant to fairness in machine learning; it is not the whole story!"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "xFxZOg55lWJE"
},
"source": [
"## Learning Objectives\n",
"\n",
"* Increase awareness of different types of biases that can manifest in model data.\n",
"* Explore feature data to proactively identify potential sources of bias before training a model.\n",
"* Evaluate model performace by subgroup rather than in aggregate."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "l-K-xqksm-X3"
},
"source": [
"## Overview\n",
"\n",
"In this exercise, you'll explore datasets and evaluate classifiers with *fairness* in mind, noting the ways undesirable biases can creep into machine learning (ML).\n",
"\n",
"Throughout, you will see **FairAware** tasks, which provide opportunities to contextualize ML processes with respect to fairness. In performing these tasks, you'll identify biases and consider the long-term impact of model predictions if these biases are not addressed."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "TXkkHYyJ98_k"
},
"source": [
"## About the Dataset and Prediction Task\n",
"\n",
"In this exercise, you'll work with the [Adult Census Income dataset](https://archive.ics.uci.edu/ml/datasets/Census+Income), which is commonly used in machine learning literature. This data was extracted from the [1994 Census bureau database](http://www.census.gov/en.html) by Ronny Kohavi and Barry Becker.\n",
"\n",
"Each example in the dataset contains the following demographic data for a set of individuals who took part in the 1994 Census:\n",
"\n",
"### Numeric Features\n",
"* `age`: The age of the individual in years.\n",
"* `fnlwgt`: The number of individuals the Census Organizations believes that set of observations represents.\n",
"* `education_num`: An enumeration of the categorical representation of education. The higher the number, the higher the education that individual achieved. For example, an `education_num` of `11` represents `Assoc_voc` (associate degree at a vocational school), an `education_num` of `13` represents `Bachelors`, and an `education_num` of `9` represents `HS-grad` (high school graduate).\n",
"* `capital_gain`: Capital gain made by the individual, represented in US Dollars.\n",
"* `capital_loss`: Capital loss mabe by the individual, represented in US Dollars.\n",
"* `hours_per_week`: Hours worked per week.\n",
"\n",
"### Categorical Features\n",
"* `workclass`: The individual's type of employer. Examples include: `Private`, `Self-emp-not-inc`, `Self-emp-inc`, `Federal-gov`, `Local-gov`, `State-gov`, `Without-pay`, and `Never-worked`.\n",
"* `education`: The highest level of education achieved for that individual.\n",
"* `marital_status`: Marital status of the individual. Examples include: `Married-civ-spouse`, `Divorced`, `Never-married`, `Separated`, `Widowed`, `Married-spouse-absent`, and `Married-AF-spouse`.\n",
"* `occupation`: The occupation of the individual. Example include: `tech-support`, `Craft-repair`, `Other-service`, `Sales`, `Exec-managerial` and more.\n",
"* `relationship`: The relationship of each individual in a household. Examples include: `Wife`, `Own-child`, `Husband`, `Not-in-family`, `Other-relative`, and `Unmarried`.\n",
"* `gender`: Gender of the individual available only in binary choices: `Female` or `Male`.\n",
"* `race`: `White`, `Asian-Pac-Islander`, `Amer-Indian-Eskimo`, `Black`, and `Other`. \n",
"* `native_country`: Country of origin of the individual. Examples include: `United-States`, `Cambodia`, `England`, `Puerto-Rico`, `Canada`, `Germany`, `Outlying-US(Guam-USVI-etc)`, `India`, `Japan`, and more.\n",
"\n",
"### Prediction Task\n",
"The prediction task is to **determine whether a person makes over $50,000 US Dollar a year.**\n",
"\n",
"### Label\n",
"* `income_bracket`: Whether the person makes more than $50,000 US Dollars annually.\n",
"\n",
"### Notes on Data Collection\n",
"\n",
"All the examples extracted for this dataset meet the following conditions: \n",
"* `age` is 16 years or older.\n",
"* The adjusted gross income (used to calculate `income_bracket`) is greater than $100 USD annually.\n",
"* `fnlwgt` is greater than 0.\n",
"* `hours_per_week` is greater than 0.\n",
"\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "I0RMIktKy8xX"
},
"source": [
"## Setup\n",
"\n",
"First, we should ensure that this Colaboratory notebook will run on TensorFlow 2.x."
]
},
{
"cell_type": "code",
"metadata": {
"id": "MelAK2u6d-xx"
},
"source": [
"#@title Run on TensorFlow 2.x\n",
"%tensorflow_version 2.x\n",
"from __future__ import absolute_import, division, print_function, unicode_literals"
],
"execution_count": 2,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "jUsgiVsUeKRR"
},
"source": [
"Next, we'll import the necessary modules to run the code in the rest of this Colaboratory notebook. \n",
"\n",
"In addition to importing the usual libraries, this setup code cell also installs [Facets](https://pair-code.github.io/facets/), an open-source tool created by [PAIR](https://research.google/teams/brain/pair/) that contains two robust visualizations we'll be using to aid in understanding and analyzing ML datasets."
]
},
{
"cell_type": "code",
"metadata": {
"id": "2e_0DJJ8zE29",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "25306464-0dc5-4b5f-f0b3-3473c4ea2035"
},
"source": [
"#@title Import revelant modules and install Facets\n",
"import numpy as np\n",
"import pandas as pd\n",
"import tensorflow as tf\n",
"from tensorflow.keras import layers\n",
"from matplotlib import pyplot as plt\n",
"from matplotlib import rcParams\n",
"import seaborn as sns\n",
"\n",
"# The following lines adjust the granularity of reporting. \n",
"pd.options.display.max_rows = 10\n",
"pd.options.display.float_format = \"{:.1f}\".format\n",
"\n",
"from google.colab import widgets\n",
"# For facets\n",
"from IPython.core.display import display, HTML\n",
"import base64\n",
"!pip install facets-overview==1.0.0\n",
"from facets_overview.feature_statistics_generator import FeatureStatisticsGenerator"
],
"execution_count": 3,
"outputs": [
{
"output_type": "stream",
"text": [
"Collecting facets-overview==1.0.0\n",
" Downloading facets_overview-1.0.0-py2.py3-none-any.whl (24 kB)\n",
"Requirement already satisfied: protobuf>=3.7.0 in /usr/local/lib/python3.7/dist-packages (from facets-overview==1.0.0) (3.17.3)\n",
"Requirement already satisfied: pandas>=0.22.0 in /usr/local/lib/python3.7/dist-packages (from facets-overview==1.0.0) (1.1.5)\n",
"Requirement already satisfied: numpy>=1.16.0 in /usr/local/lib/python3.7/dist-packages (from facets-overview==1.0.0) (1.19.5)\n",
"Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.22.0->facets-overview==1.0.0) (2.8.1)\n",
"Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.22.0->facets-overview==1.0.0) (2018.9)\n",
"Requirement already satisfied: six>=1.9 in /usr/local/lib/python3.7/dist-packages (from protobuf>=3.7.0->facets-overview==1.0.0) (1.15.0)\n",
"Installing collected packages: facets-overview\n",
"Successfully installed facets-overview-1.0.0\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "-xgIRapb5LaQ"
},
"source": [
"### Load the Adult Dataset\n",
"\n",
"With the modules now imported, we can load the Adult dataset into a pandas DataFrame data structure."
]
},
{
"cell_type": "code",
"metadata": {
"id": "TeCNVvVUVS0P",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "7e063aa1-c098-4bc3-d044-eeaf058f31a5"
},
"source": [
"COLUMNS = [\"age\", \"workclass\", \"fnlwgt\", \"education\", \"education_num\",\n",
" \"marital_status\", \"occupation\", \"relationship\", \"race\", \"gender\",\n",
" \"capital_gain\", \"capital_loss\", \"hours_per_week\", \"native_country\",\n",
" \"income_bracket\"]\n",
"\n",
"train_csv = tf.keras.utils.get_file('adult.data', \n",
" 'https://download.mlcc.google.com/mledu-datasets/adult_census_train.csv')\n",
"test_csv = tf.keras.utils.get_file('adult.data', \n",
" 'https://download.mlcc.google.com/mledu-datasets/adult_census_test.csv')\n",
"\n",
"train_df = pd.read_csv(train_csv, names=COLUMNS, sep=r'\\s*,\\s*', \n",
" engine='python', na_values=\"?\")\n",
"test_df = pd.read_csv(test_csv, names=COLUMNS, sep=r'\\s*,\\s*', skiprows=[0],\n",
" engine='python', na_values=\"?\")"
],
"execution_count": 4,
"outputs": [
{
"output_type": "stream",
"text": [
"Downloading data from https://download.mlcc.google.com/mledu-datasets/adult_census_train.csv\n",
"3981312/3974305 [==============================] - 0s 0us/step\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "coilRN-hooja"
},
"source": [
"## Analyzing the Adult Dataset with Facets\n",
"\n",
"As mentioned in MLCC, it is important to understand your dataset *before* diving straight into the prediction task. \n",
"\n",
"Some important questions to investigate when auditing a dataset for fairness:\n",
"\n",
"* **Are there missing feature values for a large number of observations?**\n",
"* **Are there features that are missing that might affect other features?**\n",
"* **Are there any unexpected feature values?**\n",
"* **What signs of data skew do you see?**"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "9yCIuAqWA1Pm"
},
"source": [
"To start, we can use [Facets Overview](https://pair-code.github.io/facets/), an interactive visualization tool that can help us explore the dataset. With Facets Overview, we can quickly analyze the distribution of values across the Adult dataset."
]
},
{
"cell_type": "code",
"metadata": {
"id": "MW-qryqs1gig",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"outputId": "e8c16772-44dc-4567-ee36-bf38fdd280f6"
},
"source": [
"#@title Visualize the Data in Facets\n",
"fsg = FeatureStatisticsGenerator()\n",
"dataframes = [\n",
" {'table': train_df, 'name': 'trainData'}]\n",
"censusProto = fsg.ProtoFromDataFrames(dataframes)\n",
"protostr = base64.b64encode(censusProto.SerializeToString()).decode(\"utf-8\")\n",
"\n",
"\n",
"HTML_TEMPLATE = \"\"\"<script src=\"https://cdnjs.cloudflare.com/ajax/libs/webcomponentsjs/1.3.3/webcomponents-lite.js\"></script>\n",
" <link rel=\"import\" href=\"https://raw.githubusercontent.com/PAIR-code/facets/1.0.0/facets-dist/facets-jupyter.html\">\n",
" <facets-overview id=\"elem\"></facets-overview>\n",
" <script>\n",
" document.querySelector(\"#elem\").protoInput = \"{protostr}\";\n",
" </script>\"\"\"\n",
"html = HTML_TEMPLATE.format(protostr=protostr)\n",
"display(HTML(html))"
],
"execution_count": 5,
"outputs": [
{
"output_type": "display_data",
"data": {
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"<script src=\"https://cdnjs.cloudflare.com/ajax/libs/webcomponentsjs/1.3.3/webcomponents-lite.js\"></script>\n",
" <link rel=\"import\" href=\"https://raw.githubusercontent.com/PAIR-code/facets/1.0.0/facets-dist/facets-jupyter.html\">\n",
" <facets-overview id=\"elem\"></facets-overview>\n",
" <script>\n",
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" </script>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "91wjnZFpPWw-"
},
"source": [
"### FairAware Task #1\n",
"\n",
"Review the descriptive statistics and histograms for each numerical and continuous feature. Click the **Show Raw Data** button above the histograms for categorical features to see the distribution of values per category.\n",
"\n",
"Then, try to answer the following questions from earlier:\n",
"\n",
"1. Are there missing feature values for a large number of observations?\n",
"2. Are there features that are missing that might affect other features?\n",
"3. Are there any unexpected feature values?\n",
"4. What signs of data skew do you see?"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "KlF-lQ8yQ69b"
},
"source": [
"### Solution\n",
"\n",
"Click below for some insights we uncovered."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "xX_qjj5AQ_Hb"
},
"source": [
"We can see from reviewing the **missing** column that the following categorical features contain missing values:\n",
"\n",
"* workclass\n",
"* occupation\n",
"\n",
"Now, because it's only a small percentage of samples that contain either a missing workclass value or occupation value, we can safely drop those rows from the data set. If that percentage was much higher, then we would have to consider using a different data set that is more complete. \n",
"\n",
"Luckily, in Pandas, there is a convenient way to drop any row containing a missing value in the data set:\n",
"\n",
"```\n",
"# pandas.DataFrame.dropna(how=\"any\", axis=0, inplace=True)\n",
"```\n",
"We will use this method prior to training the model when we convert a Pandas DataFrame to a Numpy array.\n",
"\n",
"As for the remaining data that does not contain any missing values: if we look at the min/max values and histograms for each numeric feature, then we can pinpoint any extreme outliers in our data set. \n",
"\n",
"For `hours_per_week`, we can see that the minimum is 1, which might be a bit surprising, given that most jobs typically require multiple hours of work per week. For `capital_gain` and `capital_loss`, we can see that over 90% of values are 0. Given that capital gains/losses are only registered by individuals who make investments, it's certainly plausible that less than 10% of examples would have nonzero values for these feature, but we may want to take a closer look to verify the values for these features are valid.\n",
"\n",
"In looking at the histogram for gender, we see that over two-thirds (approximately 67%) of examples represent males. This strongly suggests data skew, as we would expect the breakdown between genders to be closer to 50/50."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "hKj2hz-Sql7V"
},
"source": [
"### A Deeper Dive\n",
"\n",
"To futher explore the dataset, we can use [Facets Dive](https://pair-code.github.io/facets/), a tool that provides an interactive interface where each individual item in the visualization represents a data point. But to use Facets Dive, we need to convert the data to a JSON array.\n",
"Thankfully the DataFrame method `to_json()` takes care of this for us.\n",
"\n",
"Run the cell below to perform the data transform to JSON and also load Facets Dive. "
]
},
{
"cell_type": "code",
"metadata": {
"id": "stlklrG_xssF",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 617
},
"outputId": "a5be1c64-88cf-4ccf-8114-c422596150a7"
},
"source": [
"#@title Set the Number of Data Points to Visualize in Facets Dive\n",
"\n",
"SAMPLE_SIZE = 5000 #@param\n",
" \n",
"train_dive = train_df.sample(SAMPLE_SIZE).to_json(orient='records')\n",
"\n",
"HTML_TEMPLATE = \"\"\"<script src=\"https://cdnjs.cloudflare.com/ajax/libs/webcomponentsjs/1.3.3/webcomponents-lite.js\"></script>\n",
" <link rel=\"import\" href=\"https://raw.githubusercontent.com/PAIR-code/facets/1.0.0/facets-dist/facets-jupyter.html\">\n",
" <facets-dive id=\"elem\" height=\"600\"></facets-dive>\n",
" <script>\n",
" var data = {jsonstr};\n",
" document.querySelector(\"#elem\").data = data;\n",
" </script>\"\"\"\n",
"html = HTML_TEMPLATE.format(jsonstr=train_dive)\n",
"display(HTML(html))"
],
"execution_count": 6,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/html": [
"<script src=\"https://cdnjs.cloudflare.com/ajax/libs/webcomponentsjs/1.3.3/webcomponents-lite.js\"></script>\n",
" <link rel=\"import\" href=\"https://raw.githubusercontent.com/PAIR-code/facets/1.0.0/facets-dist/facets-jupyter.html\">\n",
" <facets-dive id=\"elem\" height=\"600\"></facets-dive>\n",
" <script>\n",
" var data = 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-spouse\",\"occupation\":\"Adm-clerical\",\"relationship\":\"Husband\",\"race\":\"White\",\"gender\":\"Male\",\"capital_gain\":0,\"capital_loss\":0,\"hours_per_week\":40,\"native_country\":\"United-States\",\"income_bracket\":\"<=50K\"},{\"age\":60,\"workclass\":\"Private\",\"fnlwgt\":205934,\"education\":\"HS-grad\",\"education_num\":9,\"marital_status\":\"Separated\",\"occupation\":\"Other-service\",\"relationship\":\"Not-in-family\",\"race\":\"White\",\"gender\":\"Female\",\"capital_gain\":0,\"capital_loss\":0,\"hours_per_week\":25,\"native_country\":\"United-States\",\"income_bracket\":\"<=50K\"},{\"age\":53,\"workclass\":\"Private\",\"fnlwgt\":71772,\"education\":\"Doctorate\",\"education_num\":16,\"marital_status\":\"Divorced\",\"occupation\":\"Prof-specialty\",\"relationship\":\"Not-in-family\",\"race\":\"White\",\"gender\":\"Male\",\"capital_gain\":0,\"capital_loss\":0,\"hours_per_week\":50,\"native_country\":\"United-States\",\"income_bracket\":\"<=50K\"},{\"age\":40,\"workclass\":\"Private\",\"fnlwgt\":216237,\"education\":\"Bachelors\",\"education_num\":13,\"marital_status\":\"Divorced\",\"occupation\":\"Exec-managerial\",\"relationship\":\"Not-in-family\",\"race\":\"White\",\"gender\":\"Female\",\"capital_gain\":0,\"capital_loss\":0,\"hours_per_week\":45,\"native_country\":\"United-States\",\"income_bracket\":\">50K\"},{\"age\":29,\"workclass\":\"Private\",\"fnlwgt\":210959,\"education\":\"Assoc-acdm\",\"education_num\":12,\"marital_status\":\"Married-civ-spouse\",\"occupation\":\"Adm-clerical\",\"relationship\":\"Husband\",\"race\":\"White\",\"gender\":\"Male\",\"capital_gain\":0,\"capital_loss\":0,\"hours_per_week\":40,\"native_country\":\"United-States\",\"income_bracket\":\"<=50K\"},{\"age\":44,\"workclass\":\"Private\",\"fnlwgt\":228320,\"education\":\"HS-grad\",\"education_num\":9,\"marital_status\":\"Married-civ-spouse\",\"occupation\":\"Tech-support\",\"relationship\":\"Husband\",\"race\":\"White\",\"gender\":\"Male\",\"capital_gain\":0,\"capital_loss\":0,\"hours_per_week\":45,\"native_country\":\"United-States\",\"income_bracket\":\">50K\"},{\"age\":29,\"workclass\":\"Private\",\"fnlwgt\":114158,\"education\":\"Bachelors\",\"education_num\":13,\"marital_status\":\"Never-married\",\"occupation\":\"Exec-managerial\",\"relationship\":\"Own-child\",\"race\":\"White\",\"gender\":\"Female\",\"capital_gain\":3325,\"capital_loss\":0,\"hours_per_week\":10,\"native_country\":\"United-States\",\"income_bracket\":\"<=50K\"},{\"age\":36,\"workclass\":\"State-gov\",\"fnlwgt\":77146,\"education\":\"Masters\",\"education_num\":14,\"marital_status\":\"Never-married\",\"occupation\":\"Prof-specialty\",\"relationship\":\"Not-in-family\",\"race\":\"White\",\"gender\":\"Male\",\"capital_gain\":0,\"capital_loss\":0,\"hours_per_week\":40,\"native_country\":\"United-States\",\"income_bracket\":\"<=50K\"},{\"age\":26,\"workclass\":\"Private\",\"fnlwgt\":302097,\"education\":\"HS-grad\",\"education_num\":9,\"marital_status\":\"Married-civ-spouse\",\"occupation\":\"Machine-op-inspct\",\"relationship\":\"Husband\",\"race\":\"White\",\"gender\":\"Male\",\"capital_gain\":3464,\"capital_loss\":0,\"hours_per_week\":48,\"native_country\":\"United-States\",\"income_bracket\":\"<=50K\"},{\"age\":27,\"workclass\":\"Private\",\"fnlwgt\":269444,\"education\":\"HS-grad\",\"education_num\":9,\"marital_status\":\"Divorced\",\"occupation\":\"Handlers-cleaners\",\"relationship\":\"Not-in-family\",\"race\":\"White\",\"gender\":\"Male\",\"capital_gain\":0,\"capital_loss\":0,\"hours_per_week\":40,\"native_country\":\"United-States\",\"income_bracket\":\"<=50K\"},{\"age\":46,\"workclass\":\"Private\",\"fnlwgt\":72896,\"education\":\"Some-college\",\"education_num\":10,\"marital_status\":\"Married-civ-spouse\",\"occupation\":\"Exec-managerial\",\"relationship\":\"Husband\",\"race\":\"White\",\"gender\":\"Male\",\"capital_gain\":0,\"capital_loss\":0,\"hours_per_week\":50,\"native_country\":\"United-States\",\"income_bracket\":\"<=50K\"},{\"age\":30,\"workclass\":\"Private\",\"fnlwgt\":167476,\"education\":\"HS-grad\",\"education_num\":9,\"marital_status\":\"Married-civ-spouse\",\"occupation\":\"Transport-moving\",\"relationship\":\"Husband\",\"race\":\"White\",\"gender\":\"Male\",\"capital_gain\":0,\"capital_loss\":0,\"hours_per_week\":50,\"native_country\":\"United-States\",\"income_bracket\":\"<=50K\"},{\"age\":27,\"workclass\":\"Private\",\"fnlwgt\":301654,\"education\":\"HS-grad\",\"education_num\":9,\"marital_status\":\"Never-married\",\"occupation\":\"Exec-managerial\",\"relationship\":\"Own-child\",\"race\":\"White\",\"gender\":\"Female\",\"capital_gain\":0,\"capital_loss\":0,\"hours_per_week\":40,\"native_country\":\"United-States\",\"income_bracket\":\"<=50K\"},{\"age\":75,\"workclass\":null,\"fnlwgt\":173064,\"education\":\"Bachelors\",\"education_num\":13,\"marital_status\":\"Married-civ-spouse\",\"occupation\":null,\"relationship\":\"Husband\",\"race\":\"White\",\"gender\":\"Male\",\"capital_gain\":0,\"capital_loss\":0,\"h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