parity-fairness 0.1.20

Measure bias from data and machine learning models.

Parity

This repository contains codes that demonstrate the use of fairness metrics, bias mitigations and explainability tool.

Installation

In order for the explainability modules to work, first you have to install shap through conda like so:

foo@bar:~$ conda install -c conda-forge shap

Install using:

foo@bar:~$ pip install parity-fairness

Bias Measurement Usage

Setup the data such that the target column is a binary string target. Then find out which features are the privileged categories and which values are privileged values. Afterwards, feed them into the function called show_bias like:

from parity.fairness_metrics import show_bias

priv_category = 'Race-White'
priv_value = 'True'
target_label = 'high pay'
unencoded_target_label = 'True'
cols_to_drop = ''

show_bias(data, priv_category, priv_value, target_label, unencoded_target_label, cols_to_drop)

Bias and Fairness

A common problem with most machine learning models is bias from data. This notebook shows how to measure those biases and perform bias mitigation. A python package called aif360 can give us metrics and algorithms for bias measurement and mitigation

Metrics

• Statistical Parity Difference
• Equal Opportunity Difference
• Average Absolute Odds Difference
• Disparate Impact
• Theil Index

Some metrics need predictions while others just the original dataset. This is why we will use 2 classes of the aif360 package : ClassificationMetric and BinaryLabelDatasetMetric.

For metrics that require predictions:

• Equal Opportunity Difference: equal_opportunity_difference()
• Average Absolute Odds Difference: average_abs_odds_difference()
• Theil Index : theil_index()

For metrics that don't require predictions:

• Statistical Parity Difference: statistical_parity_difference()
• Disparate Impact: disparate_impact()