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name: fairness-workflows description: "Configure and inspect fairness-aware AutoML workflows in mljar-supervised." disable-model-invocation: true metadata: disco-role: operating license: MIT

fairness-workflows

Use this sub-skill when the task asks for fairness-aware supervised.AutoML training or inspection with sensitive_features, fairness_metric, fairness_threshold, privileged_groups, or underprivileged_groups.

Route here for

• Binary classification, multiclass classification, or regression training that passes sensitive features to AutoML.fit().
• Choosing a task-compatible fairness metric and threshold.
• Declaring privileged and underprivileged groups, including multiple sensitive columns.
• Interpreting fairness leaderboard columns, fairness summaries, certificates, and fairness fields in report_structured() output.
• Diagnosing fairness-specific errors such as invalid metric names, task/metric mismatches, missing thresholds, or sensitive-feature shape issues.

Read first

• Fairness guide for API shape, workflow recipes, reports, and safe patterns.
• Fairness metrics for metric names, default thresholds, directionality, and task compatibility.
• Troubleshooting for common failure modes and corrective actions.
• Synthetic smoke helper for a tiny no-network fit plus structured-report fairness signal check.

Boundaries

• For generic AutoML mode, algorithm, validation, time-budget, scoring, and prediction setup, route to ../training-core/.
• For generic report serialization, result-directory layout, model-specific structured reports, loading saved runs, and explainability artifacts, route to ../artifacts-reports/.
• For generated Mercury apps, local serving, or publishing, route to ../app-deployment/.
• For package install/import and optional dependency issues, start from ../../references/package-overview.md and ../../references/troubleshooting.md.

Do not run large or network-backed fairness examples as runtime checks. Use the bundled synthetic helper or a user-provided local dataset instead.