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name: detectors-and-explainers description: "Use AIF360 MDSS and FACTS subgroup detectors plus metric text and JSON explainers." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

AIF360 Detectors and Explainers

Use this sub-skill when a task asks for subgroup bias scanning, MDSS/FACTS detectors, or text/JSON explanations of AIF360 metric objects.

Start here

1. Read mdss-workflow.md for legacy and sklearn bias_scan signatures, inputs, modes, scoring choices, and output handling.
2. Read facts-workflow.md for optional FACTS counterfactual subgroup-recourse workflows and dependency requirements.
3. Read explainer-reference.md for MetricTextExplainer and MetricJSONExplainer usage with metric objects.
4. Read troubleshooting.md when labels, probabilities, indexes, optional extras, or metric objects do not match.
5. Use mdss_smoke.py or explainer_smoke.py for no-download base smoke checks.

Route by user intent

User asks for...	Use this route
"Find a subgroup with unusually high error or bias"	Use MDSS bias_scan; read mdss-workflow.md.
"Score a known subgroup with MDSS inside classification metrics"	Use MDSSClassificationMetric via datasets-and-metrics, then return here for scan interpretation if needed.
"Find subgroups that have worse recourse/counterfactual action costs"	Use optional FACTS; read facts-workflow.md.
"Explain what this metric means in text or JSON"	Use explainer-reference.md.
"Compute fairness metrics before scanning"	Route to ../datasets-and-metrics/SKILL.md for legacy metrics or ../sklearn-interface/SKILL.md for pandas metrics.
"Mitigate bias after detecting a problem"	Route to ../mitigation-algorithms/SKILL.md.

Detector/explainer distinction

• MDSS detectors search for subgroups whose observed outcomes differ from expectations under a scoring function.
• FACTS searches for subgroups with unequal counterfactual recourse cost or effectiveness; it is optional-extra gated.
• Metric explainers wrap an existing AIF360 metric object and render method outputs in human-readable text or structured JSON; they do not compute new metrics independently.

Safe smoke checks

python sub-skills/detectors-and-explainers/scripts/mdss_smoke.py --json
python sub-skills/detectors-and-explainers/scripts/explainer_smoke.py --json

Both scripts use synthetic in-memory data and require only the base package surface that was verified for this skill construction run.