aequitas

Bias Auditing & Fair ML Toolkit

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Builder

dssg

dssg • individual

Stars

757

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Forks

123

Using upstream fork count

Open Issues

Activity Score

0/100

0 commits in 30d

Created

Feb 13, 2018

Project creation date

README Summary

Aequitas is an open-source bias audit toolkit for machine learning developers, analysts, and policymakers to audit machine learning models for discrimination and bias. It provides comprehensive fairness metrics and visualizations to assess model performance across different demographic groups. The toolkit supports both individual and group fairness evaluations with an intuitive Python API and web interface.

AI Dev Skills

Unmapped

Tags

Algorithmic FairnessBias Detection and MitigationMachine Learning Model AuditingStatistical Parity AnalysisDemographic Parity AssessmentEqualized Odds EvaluationClassification Performance AnalysisGroup Fairness MetricsHealthcareRegulatory Compliance ReportingAI SafetyFinTechML Model Bias AuditingCredit Scoring Bias DetectionHealthcare Algorithm Equity AnalysisRecidivism Prediction FairnessAI EthicsOn-premiseFairness Assessment in Hiring AlgorithmsCriminal JusticeInsuranceResponsible AIGovernmentLegal TechTabularAlgorithmic AccountabilityHR TechSelf-hostedPython

Recent Activity

Updated 2 months ago

7 Days

30 Days

90 Days

Quality

beta

Quality: medium
Maturity: beta

PM Skills

Developer Platform

Languages

Python100.0%

Timeline

Project created: Feb 13, 2018
Forked: Mar 22, 2026
Your last push: 2 months ago
Upstream last push: 2 months ago
Tracked since: Feb 3, 2026

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