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Evidence record 956 · automatically gathered

AgentFairBench: Do LLM Agents Discriminate When They Act?

Large language model (LLM) agents increasingly take actions (screening applicants, recommending credit, triaging patients), yet fairness for LLMs is still measured by grading answers. We introduce AgentFairBench, a cheap, reproducible, multi-domain benchmark for demographic disparity in the actions of LLM agents. Grounded in a companion framework, the Bias Conduction Framework (BCF, restated here), it spans three regulator-anchored domains: hiring, lending, and medical triage. Synthetic, demogra

Record details

Published: 15 June 2026
Source: arXiv
Category: Research
Topics: Bias & fairness · Regulation · Healthcare · Agents & autonomy
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 (15 June 2026), “AgentFairBench: Do LLM Agents Discriminate When They Act?,” evidence record 956, https://ethics.ai/record/956 (originally published by arXiv).

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