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name: mitigation-algorithms description: "Choose and run AIF360 legacy bias mitigation algorithms with correct lifecycle stage, data contracts, optional extras, and metric checks." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

AIF360 Mitigation Algorithms

Use this sub-skill when a task asks for legacy aif360.algorithms mitigation with AIF360 datasets: preprocessing, inprocessing, postprocessing, deterministic reranking, optional extras, fit/transform/predict patterns, and fairness-metric validation.

Start here

1. Pick the lifecycle stage and class in algorithm-selection.md.
2. Follow end-to-end usage patterns in workflows.md.
3. Check optional dependency status and install hints in optional-algorithms.md.
4. Diagnose install/import, optional-extra, data/API, score, and workflow failures in troubleshooting.md.
5. For a base-safe smoke, run reweighing_smoke.py with --help before --json.

Route neighboring tasks

• Dataset construction, BinaryLabelDataset fields, group dictionaries, raw dataset wrappers, and metrics: datasets-and-metrics.
• aif360.sklearn estimators, pandas protected-attribute indexes, scorers, and sklearn pipelines: sklearn-interface.
• MDSS/FACTS subgroup detection or metric text/JSON explainers: detectors-and-explainers.

Operating rules

• Select mitigation by where it acts: preprocessing modifies data before model training; inprocessing trains a mitigated model; postprocessing adjusts already-produced predictions; reranking reorders scored candidates.
• Keep true labels, predicted labels, scores, protected attributes, favorable/unfavorable labels, and privileged/unprivileged group values aligned across every dataset copy.
• Validate with AIF360 metrics before and after mitigation; a class name is not evidence that fairness improved.
• Optional extras were intentionally not installed in the construction environment. Mark optional workflows as optional/unverified until the user's runtime installs the named extra and passes a small smoke.