Fairness Indicators
Fairness Indicators is a library that enables easy computation of commonly-identified fairness metrics for binary and multiclass classifiers. With the Fairness Indicators tool suite, you can:
- Compute commonly-identified fairness metrics for classification models
- Compare model performance across subgroups to a baseline, or to other models
- Use confidence intervals to surface statistically significant disparities
- Perform evaluation over multiple thresholds
Use Fairness Indicators via the:
eval_config_pbtxt = """ model_specs { label_key: "%s" } metrics_specs { metrics { class_name: "FairnessIndicators" config: '{ "thresholds": [0.25, 0.5, 0.75] }' } metrics { class_name: "ExampleCount" } } slicing_specs {} slicing_specs { feature_keys: "%s" } options { compute_confidence_intervals { value: False } disabled_outputs{values: "analysis"} } """ % (LABEL_KEY, GROUP_KEY)
