This repository contains a sample implementation of Gradient Feature Auditing (GFA) meant to be generalizable to most datasets.

A fully independent and impartial audit to ensure compliance with the bias audit requirements for New York City Local Law 144 and upcoming regulations.

A bespoke AI risk audit solution tailor-made to identify your enterprise project’s AI risks, comprising deep technical, quantitative analysis.

Open Sourced Bias Testing for Generalized Machine Learning Applications audit-AI is a Python library built on top of pandas and sklearn that implements fairness-aware machine learning algorithms. audit-AI was developed by the Data Science team at pymetrics

Aequitas is an open source bias and fairness audit toolkit that is an intuitive and easy to use addition to the machine learning workflow, enabling users to seamlessly test models for several bias and fairness metrics in relation to multiple population sub-groups. Aequitas facilitates informed and equitable decisions around developing and deploying algorithmic decision making […]