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Paper questions reliability of ML fairness audits with missing data

By PulseAugur Editorial · [1 sources] ·

A new paper explores the reliability of fairness audits in machine learning when data on protected attributes is incomplete. Researchers found that missing protected-label data often does not significantly alter the recommendations of common mitigation methods. However, threshold optimization can inadvertently lead to intersectional harm, even when fairness gains are observed on single axes. AI

IMPACT Highlights potential pitfalls in evaluating ML model fairness, urging caution in interpreting audit results with incomplete data.

RANK_REASON The cluster contains a research paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Paper questions reliability of ML fairness audits with missing data

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The cluster contains a research paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Yash Vardhan Tomar ·

    How Reliable are Fairness Audits with Unreliable Data?

    arXiv:2506.23033v2 Announce Type: replace-cross Abstract: Fairness audits are a key component of responsible machine-learning deployment. Yet, the reliability of audit recommendations under incomplete protected-label access is still poorly understood. In this work, we focused on …

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