rai-check-ml
Fairness, robustness, data quality, and production drift audits for tabular ML models.
from rai_audit.ml import ClassificationAudit, DriftAudit, RegressionAudit
DriftAudit compares a reference window with a current batch. It checks numeric
feature distributions, prediction distributions, sensitive-feature subgroup
composition, and classification error-rate changes per sensitive group. Numeric
drift evidence includes corrected KS p-values, population stability index, and
Jensen-Shannon divergence.
Classification fairness checks include equalized odds, calibration by group when probabilities are available, Wilson confidence intervals, and explicit warnings for undersized groups. Data-quality checks include common PII patterns, numeric outliers, and target-deterministic features.
Use split_data_quality_findings to catch entity overlap and exact duplicate rows
across train and test datasets:
from rai_audit.ml import split_data_quality_findings
findings = split_data_quality_findings(train, test, id_columns=["patient_id"])
See examples/ml_drift_monitoring/batch_monitor.py
and examples/mlops_integrations/ for monitoring examples.
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Release files for rai-check-ml 0.0.2
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