Fairness Toolkits: AIF360, Fairlearn and Aequitas

Fairness toolkits make metrics and mitigation algorithms easier to run, but their outputs depend on the data, labels, group definitions, and fairness criteria chosen by the team. IBM’s AI Fairness 360 (AIF360) includes fairness metrics plus pre-, in-, and post-processing algorithms. Its APIs use dataset structures such as BinaryLabelDataset for many workflows; users must map labels, protected attributes, and favorable outcomes correctly. Reweighing changes instance weights before training, while other algorithms operate during or after model fitting. Fairlearn supports assessment and mitigation for scikit-learn-style workflows. MetricFrame disaggregates chosen metrics across sensitive-feature groups and can show intersections. ExponentiatedGradient trains a model under a specified fairness constraint and objective. ThresholdOptimizer post-processes scores by applying group-specific thresholds under a selected constraint; it requires sensitive features and may involve randomized predictions. These are technical procedures, not a determination that the constraint is legally or ethically correct. Aequitas focuses on bias and fairness audit reporting. It accepts prediction scores, labels, and group attributes, then reports group disparities under selected metrics and reference groups. Like all toolkits, results depend on how input categories, decision thresholds, and reference groups are defined. Aequitas does not automatically identify whether the target variable is a problematic proxy or whether a metric is appropriate for a particular legal context. Select a tool after defining the decision and harm. Check supported data structures, multi-class or regression needs, group intersections, sample-size limits, and integration requirements. Pin a package version and validate an example manually. Compare baseline and mitigated results on held-out data, report tradeoffs and uncertainty, and document why a metric or constraint was chosen. A library makes analysis reproducible; it cannot make the underlying judgment for you.