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)