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Evidence record 444 · automatically gathered

Sequential Fairness Auditing with Limited Output Access

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

Record details

Published: 29 June 2026
Source: arXiv
Category: Research
Topics: Bias & fairness · Regulation · Transparency
Retrieved: 14 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

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How to cite this record

ethics.ai (29 June 2026), “Sequential Fairness Auditing with Limited Output Access,” evidence record 444, https://ethics.ai/record/444 (originally published by arXiv).

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