What Must a Fairness Audit Report When Demographic Data Is Incomplete?

תקציר מקורי באנגליתarXiv:2506.23033v5 Announce Type: replace Abstract: Fairness audits are a key component of responsible machine-learning deployment. Yet what such an audit must disclose, when the protected labels it depends on are incomplete, remains unsettled. In this work, we focused on the rates a fairness audit publishes and on what an oversight reader needs beside them. We paired every published rate with a matched baseline drawn from the same audit, one hiding protected labels and one varying only the run seed. Across ACS/Folktables tasks, missingness settings that kept some protected labels moved the selected mitigation less than an ordinary rerun did. At zero protected-label access, candidates collapsed to empirical risk minimization, so the apparent exception there reflected the candidate set's co