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Aequitas review

Free#15 of 29 in AI Model Explainability Software

A focused toolkit for group fairness audits, with reports and Python, CLI, and web access.

6.8/10Editor score
Aequitas6.8 Visit Aequitas

Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026

Aequitas is an open-source toolkit for auditing bias and fairness in algorithmic decision-making and predictive risk-assessment models. It is aimed at machine-learning practitioners and decision-makers who need to compare outcomes across population groups. Its stated fit spans small, mid-market, and enterprise organizations, with access through a Python API, command-line interface, or web application.

The core workflow centers on group-level metrics: Aequitas calculates disparities relative to reference groups and can use continuous, binary, or risk scores. It also accepts database-sourced data. Audits use model scores and population attributes, and error-based analyses use actual outcome labels. The output is descriptive, with tables covering fairness, bias, and group metrics; reports can be exported as PDF, while data can be exported as CSV. That makes it a fit for teams documenting group fairness audits, rather than teams seeking explanations of individual predictions.

Aequitas is free and open source, with self-hosted deployment listed alongside its web application, Linux support, and Python/CLI access. The trade-off is its focused scope: it does not offer feature attribution or counterfactuals, and model framework support is not specified. Teams whose main need is group fairness measurement and audit reporting should consider it; those requiring attribution or counterfactual explanations should look for a tool that explicitly provides those methods.

Aequitas pros and cons

  • Where it wins
    • Compares group-level fairness metrics and disparities against reference groups
    • Generates descriptive audit reports with PDF and CSV exports
    • Offers Python API, command-line interface, and web application access
  • Where it doesn't
    • Does not provide feature attribution
    • Does not provide counterfactual analysis
    • Model framework support is not specified

Aequitas fact sheet, pricing and score →

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