arXiv:2506.23033v2 Announce Type: replace 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 protected-label missingness in fairness mitigation audits. We introduced a seed-calibrated stress test to separate missingness effects from seed-to-seed movement that is already present under complete labels. Across ACS/Folktables tasks, we found that positive-availability missingness usually does not move selected mitigatio
The increasing deployment of AI systems across critical domains makes the reliability of fairness audits, especially with imperfect data, a timely and pressing concern.
Reliable fairness audits are crucial for responsible AI deployment and to prevent unintended ethical and societal biases from being embedded at scale, influencing public trust and regulatory frameworks.
This research provides a more robust methodology for evaluating fairness mitigation strategies under real-world data constraints, thereby improving the trustworthiness of AI systems deployed with less-than-perfect data.
- · AI ethicists
- · Regulatory bodies
- · Companies deploying AI
- · Users of AI systems
- · AI systems with unaddressed biases
- · Unreliable auditing methodologies
Improved methodologies will lead to more effective fairness interventions in AI systems during development and deployment.
Greater confidence in AI fairness could accelerate AI adoption in sensitive sectors and foster more sophisticated regulatory oversight.
Heightened public trust in AI could reduce societal resistance to automation and lead to broader integration of AI across socioeconomic structures.
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