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Evidence record 16133 · automatically gathered

Fairness Auditing: Lower Bounds on Company Manipulation

Fairness audits are increasingly mandated in high-stakes applications such as hiring, lending, and automated decision-making. Recent work has established fundamental impossibility results for black-box fairness auditing, showing that sufficiently expressive models can evade any auditing strategy. We complement these results by quantifying the extent of unavoidable post-audit manipulation under finite audit resources. We formulate fairness auditing as a min-max optimization between a computationa

Record details

Published: 1 August 2026
Source: arXiv fairness query
Category: Research
Topics: Bias & fairness · Transparency
Retrieved: 4 August 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.

Shared topic: Bias & fairness · Shared topic: Transparency

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Shared topic: Bias & fairness · Shared topic: Transparency

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Shared topic: Bias & fairness · Shared topic: Transparency

FairFund-Bench: Evaluating Distributive Bias in LLM Resource Allocation

arXiv cs.CY · 3 August 2026

How to cite this record

ethics.ai (1 August 2026), “Fairness Auditing: Lower Bounds on Company Manipulation,” evidence record 16133, https://ethics.ai/record/16133 (originally published by arXiv fairness query).

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