arXiv:2606.30338v1 Announce Type: new Abstract: External evaluations are becoming increasingly central to the governance of AI systems. In practice, however, independent auditors often have limited access to deployed models and must rely on query-based interactions. Most existing fairness evaluation methods assume static datasets and fixed-sample statistical tests, making them poorly suited to real-world auditing scenarios in which evidence must be collected sequentially under query constraints. In this work, we formulate fairness auditing as a tolerance-aware sequential hypothesis-testing pro
As AI systems become more ubiquitous and powerful, the need for robust and practical auditing mechanisms, especially regarding fairness, becomes critical to ensure ethical deployment and regulatory compliance.
This development addresses a critical gap in AI governance by proposing a method for continuous fairness auditing even when access to models is limited, which is crucial for real-world deployment and accountability.
Fairness evaluation for AI systems can now move beyond static, dataset-dependent checks to dynamic, query-constrained sequential auditing, enabling more effective oversight of deployed models.
- · AI auditors
- · AI ethics researchers
- · Regulatory bodies
- · Organizations deploying AI models
- · AI systems with unaddressed biases
- · Organizations relying on opaque, unaudited AI
Increased capability for external oversight and accountability of AI systems will emerge.
New standards and best practices for sequential fairness auditing will be developed and adopted across industries.
Public trust in AI systems may improve as transparent and continuous auditing mechanisms become more prevalent.
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Read at arXiv cs.AI