fairness-auditing

This paper derives lower bounds on how much a company can manipulate fairness metrics after an audit, showing that finite-budget fairness certification cannot fully eliminate post-audit manipulation.

This doctoral thesis critiques current fairness metrics in machine learning and proposes statistical hypothesis testing and structural analysis to address bias, emphasizing network and hierarchical contexts.