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

arXiv: https://arxiv.org/abs/2506.23033 · HTML: https://mlsanity.com/paper/2506.23033/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 30 references that are in the map.
- First posted on arXiv 28 Jun 2025.

## Builds on (most cited first, 18 of 18)

- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [Retiring Adult: New Datasets for Fair Machine Learning](https://mlsanity.com/paper/2108.04884/) (2021)
- [Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness](https://mlsanity.com/paper/1711.05144/) (2017)
- [Fairness without Demographics through Adversarially Reweighted Learning](https://mlsanity.com/paper/2006.13114/) (2020)
- [Fairness Under Unawareness: Assessing Disparity When Protected Class Is Unobserved](https://mlsanity.com/paper/1811.11154/) (2018)
- [Assessing Algorithmic Fairness with Unobserved Protected Class Using Data Combination](https://mlsanity.com/paper/1906.00285/) (2019)
- [Robust Optimization for Fairness with Noisy Protected Groups](https://mlsanity.com/paper/2002.09343/) (2020)
- [Characterizing Fairness Over the Set of Good Models Under Selective Labels](https://mlsanity.com/paper/2101.00352/) (2021)
- [Who Audits the Auditors? Recommendations from a field scan of the algorithmic auditing ecosystem](https://mlsanity.com/paper/2310.02521/) (2023)
- [Characterizing Intersectional Group Fairness with Worst-Case Comparisons](https://mlsanity.com/paper/2101.01673/) (2021)
- [The Unfairness of Fair Machine Learning: Levelling down and strict egalitarianism by default](https://mlsanity.com/paper/2302.02404/) (2023)
- [A Comprehensive Empirical Study of Bias Mitigation Methods for Machine Learning Classifiers](https://mlsanity.com/paper/2207.03277/) (2022)
- [Predictive Churn with the Set of Good Models](https://mlsanity.com/paper/2402.07745/) (2024)
- [Be Intentional About Fairness!: Fairness, Size, and Multiplicity in the Rashomon Set](https://mlsanity.com/paper/2501.15634/) (2025)
- [On the Impact of Random Seeds on the Fairness of Clinical Classifiers](https://mlsanity.com/paper/2104.06338/) (2021)
- [When Fair Classification Meets Noisy Protected Attributes](https://mlsanity.com/paper/2307.03306/) (2023)

## Abstract (arXiv)

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 composition. Equalized-odds threshold optimization most often regressed an intersectional subgroup, but that rate fell back to its baseline once we kept only the configurations an auditor would accept. Any accuracy it lost fell on the population as heavily as on the worst-off cell, so the mechanism is levelling down. The one effect that survived was a change in which cell is worst-off. Overall, our results highlight that a published audit rate should be reported with the baseline needed to interpret it, the candidate set it came from, and its intersectional effects, before it is treated as evidence about a deployed model.