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name: datasets-and-metrics description: "Use AIF360 legacy dataset containers and fairness metric classes for tabular protected-group analysis." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

AIF360 datasets and metrics router

Use this sub-skill when the task is about AIF360's legacy dataset objects and metric classes: constructing StructuredDataset, BinaryLabelDataset, StandardDataset, or RegressionDataset instances; loading legacy raw-dataset wrappers; defining privileged/unprivileged protected groups; and computing dataset, classification, regression, sample-distortion, or MDSS classification metrics.

Read first

• Data formats: dataset object conventions, in-memory pandas construction, built-in dataset wrapper caveats, and group/label encoding rules.
• API reference: constructor signatures, metric class selection, method groups, and optional OT metric caveats.
• Workflows: runnable patterns for synthetic datasets, prediction metric reports, raw wrappers, sample distortion, and regression metrics.
• Troubleshooting: import/install warnings, optional dependencies, raw-data failures, group/schema errors, and workflow-specific metric failures.

Fast routing

1. In-memory legacy dataset: read data formats and build a numeric, NA-free pandas DataFrame; pass label_names, protected_attribute_names, and explicit favorable_label/unfavorable_label when labels are not 1.0/0.0.
2. Built-in wrappers: read built-in dataset wrappers before calling AdultDataset, GermanDataset, CompasDataset, BankDataset, MEPSDataset19/20/21, or LawSchoolGPADataset; legacy wrappers are not safe smoke tests because public raw files may be absent or network-bound.
3. Metric choice: use BinaryLabelDatasetMetric for one true dataset, ClassificationMetric for true-vs-predicted BinaryLabelDataset pairs, SampleDistortionMetric for original-vs-distorted StructuredDataset pairs, RegressionDatasetMetric for ranked regression datasets, and MDSSClassificationMetric only when a classification metric object must score a known group.
4. Group definitions: create privileged_groups and unprivileged_groups as lists of dictionaries keyed by dataset.protected_attribute_names; the dictionary values must match the encoded numeric protected attributes.
5. Smoke check: run the bundled no-data script with python scripts/metric_report_smoke.py --pretty from this sub-skill directory, or pass its path to a Python interpreter from another working directory.

Route away when appropriate

• If the task asks for bias mitigation fit, transform, predict, postprocessing thresholds, or algorithm selection after metric diagnosis, route to mitigation-algorithms.
• If the task asks for the preferred pandas/scikit-learn interface, aif360.sklearn.datasets.fetch_*, protected attributes in pandas indexes, sklearn scorers, or sklearn pipelines, route to sklearn-interface.
• If the task asks for subgroup search, FACTS, bias scanning beyond MDSSClassificationMetric.score_groups, or metric text/JSON explainers, route to detectors-and-explainers.

Minimal operating checklist

• Confirm aif360 imports and note that base dataset/metric workflows are CPU-only.
• Keep raw benchmark data and network access out of smoke tests; use synthetic BinaryLabelDataset examples first.
• Check dataset.protected_attribute_names, dataset.privileged_protected_attributes, and dataset.unprivileged_protected_attributes before constructing metric group dictionaries.
• For ClassificationMetric, make the predicted dataset by deep-copying the true dataset and changing only labels and optionally scores; otherwise equality validation will fail.
• Mark optional-dependency metrics such as optimal transport as optional/unverified unless the required extra has been installed and tested in the current task environment.