aif360

# AIF360 Repo Skill Use this skill when a task involves the AI Fairness 360 (`aif360`) Python package: fairness datasets, group-fairness metrics, bias-mitigation algorithms, the preferred `aif360.sklearn` API, MDSS/FACTS subgroup detectors, or metric explainers. ## Install and smoke-check first Base package install: ```bash pip install aif360 python -c "import aif360; print(aif360.__version__)" ``` For local package development, use an editable install in an isolated Python environment. For optional algorithms, install the smallest named extra that matches the requested workflow rather than `aif360[all]` by default; read [install-data-and-optional-deps.md](references/install-data-and-optional-deps.md). Run the bundled base smoke when importability or optional-warning noise is unclear: ```bash python scripts/check_aif360_env.py --json ``` The smoke uses only synthetic in-memory data and does not download benchmark datasets or require optional extras. ## Route by task | Task signal | Read | | --- | --- | | Legacy `BinaryLabelDataset`, `StructuredDataset`, standard dataset wrappers, `BinaryLabelDatasetMetric`, `ClassificationMetric`, raw-data availability, or protected group dictionaries | [datasets-and-metrics](sub-skills/datasets-and-metrics/SKILL.md) | | Legacy `aif360.algorithms` preprocessing, inprocessing, postprocessing, deterministic reranking, optional mitigation extras, or before/after metric validation | [mitigation-algorithms](sub-skills/mitigation-algorithms/SKILL.md) | | Preferred pandas/sklearn API, `aif360.sklearn.datasets.fetch_*`, protected attributes in pandas indexes, sklearn metrics/scorers, sklearn estimators, or `PostProcessingMeta` | [sklearn-interface](sub-skills/sklearn-interface/SKILL.md) | | MDSS subgroup bias scan, FACTS recourse subgroup reports, `MetricTextExplainer`, or `MetricJSONExplainer` | [detectors-and-explainers](sub-skills/detectors-and-explainers/SKILL.md) | | Install/import failures, raw dataset files, optional extras, warnings, Python versions, R wrapper, or MLOps sample boundaries | Root references below | ## Core decision points - **Legacy vs sklearn API**: choose legacy APIs for `BinaryLabelDataset` and legacy algorithms; choose `aif360.sklearn` for pandas/sklearn pipelines and future-facing DataFrame workflows. - **Data availability**: AIF360 documents common benchmark datasets, but most raw files are not bundled. Do not download data unless the user approves data acquisition and terms. - **Optional extras**: many algorithms are public but extra-gated. Missing TensorFlow, fairlearn, torch, cvxpy, BlackBoxAuditing, POT, FACTS, or R/rpy2 packages are not base-install failures unless the selected workflow needs them. - **Fairness semantics**: always make `favorable_label`, `pos_label`, privileged group, unprivileged group, protected attributes, and row alignment explicit before interpreting a metric or detector result. - **Verification status**: this skill verified base CPU package imports and synthetic dataset/metric workflows. Optional-extra workflows are documented but must be installed and smoke-tested in the user's runtime before claiming execution support. ## Root references and scripts - [repo-provenance.md](references/repo-provenance.md): source snapshot and refresh baseline for this generated skill. - [repo-routing-metadata.json](references/repo-routing-metadata.json): structured metadata for repo-skills-router import. - [install-data-and-optional-deps.md](references/install-data-and-optional-deps.md): package install variants, extras, standard dataset data constraints, and construction verification status. - [troubleshooting.md](references/troubleshooting.md): cross-cutting import, optional dependency, data, API-family, and workflow recovery guidance. - [r-and-mlops-notes.md](references/r-and-mlops-notes.md): boundaries for the R package wrapper and platform integration samples. - [check_aif360_env.py](scripts/check_aif360_env.py): safe base environment diagnostic helper. ## Quick operating checklist 1. Identify whether the user is using legacy datasets or `aif360.sklearn`. 2. Confirm data source and raw-data/network permissions. 3. Confirm protected attributes, privileged/unprivileged groups, and favorable labels. 4. Install only the base package plus selected extras needed by the workflow. 5. Run a bundled synthetic smoke before running real data or optional training. 6. Use metrics or detectors to diagnose bias, then route to mitigation only if the user asks to change data/model/predictions. 7. If the current package commit/version differs from [repo-provenance.md](references/repo-provenance.md), refresh this skill before relying on exact API claims.