Aequitas
Aequitas: A focused toolkit for group fairness audits, with reports and Python, CLI, and web access. Ranked #15 of 29 in AI Model Explainability Software by our editors (6.8/10); pricing: Free plan; best for teams auditing group fairness and bias.
At a glance
- Editor score6.8 / 10
- PricingFree plan
- Best forTeams auditing group fairness and bias
- Free planYes
- Paid fromNone
- Feature attributionNo
- Facts checked28 Sep 2026
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
Our verdict on Aequitas
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.
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Aequitas pricing
Aequitas fact sheet
| Free plan | Yes |
|---|---|
| Paid from | None |
| Feature attribution | No |
| Counterfactual explanations | No |
| Fairness analysis | Yes |
| Audit reports | Yes |
| Deployment options | Self_hosted |
| Supported model types | Binary, continuous, and risk-score inputs; model frameworks not specified |
| Deployment | Self-hosted |
| Platforms | Web, Linux |
| Built for | Small business, Mid-market, Enterprise (editorial estimate) |
| Pricing | Free plan |
| Website | dssg.github.io |
| Facts checked | 28 Sep 2026 |
Alternatives to Aequitas
- AIX360A broad, self-hosted Python toolkit for local, global, and time-series model explanations.9.1
- Arize AIProduction-focused explainability pairs feature attribution with fairness monitoring and model comparisons.8.5
- Fiddler AIA tracing and evaluation platform for teams debugging LLM and agent workflows.7.4
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Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026
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