Efficient Active Auditing of Multi-Group Fairness with Bias Probes
The research introduces ALeBi, an active auditing framework for machine learning models to detect multi-group fairness biases without revealing model details. It uses 'bias probes'—structured comparisons—to target specific unfairness patterns efficiently. This allows auditors to estimate fairness metrics using a minimal number of queries, providing rigorous complexity guarantees.