Aequitas

dssg.github.io · checked 14 Nov 2026

#1 · AI Model Explainability Software

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How it plugs in

—Public API

NoOpen source

YesFree tier

Signal on AI Model Explainability Software6.3#1 of 29

Aequitas audits machine-learning models for discrimination and bias to inform decisions about predictive risk-assessment tools.

Pricing

AequitasOpen-source toolkit · Python libraryFree

As github.com lists them, checked 14 Nov 2026.

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Runs on

Web
Windows
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Linux
iOS
Android

Facts

Website: dssg.github.io
Price: Free tier
Free trial: No
Audit capabilities: It helps identify model biases, compare bias disparity between population groups, and visualize metrics and disparities.
Database support: The CLI can use database input configured with database credentials and an SQL query.
Extensibility: Users can add their own Fair ML methods through defined interfaces and method signatures.
Fairness methods: Aequitas Flow includes pre-, in-, and post-processing Fair ML methods for experimentation.
Input requirements: An audit uses a pandas DataFrame with a target label, model score, and categorical sensitive attributes; at least one sensitive attribute is required.
Integration: The experimentation workflow includes Optuna hyperparameter-optimization capabilities.
Intended users: The toolkit is aimed at machine-learning developers, analysts, and policymakers.
License: The GitHub repository identifies the project license as MIT.
Local deployment: The web application can be run locally after installing Aequitas.
Metric flexibility: Users can select confusion-matrix-based metrics relevant to their use case.
Plots and reporting: The toolkit provides plots for audit and experiment outcomes; its webapp and CLI can produce bias reports.
Python compatibility: The documentation states compatibility with Python 3.6 and later.

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