Toward Human Rights Benchmarking for LLMs: A Pilot Methodology
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Computer Science > Machine Learning
Title:Toward Human Rights Benchmarking for LLMs: A Pilot Methodology
Abstract:Large language models (LLMs) increasingly mediate legal determinations over what human rights are realized, and how. Yet, no evaluation benchmark exists to assess whether they can reason correctly about human rights law. To this end, we report our efforts to develop a robust and scalable methodology for creating HumRightsBench: the first expert-validated, scenario-based benchmark for evaluating reasoning grounded in the obligation structure of international human rights law. We adapt the IRAC framework for legal reasoning to better suit the unique reasoning patterns of human rights work (substituting P, "proposing remedies," for C, "legal conclusion," yielding IRAP) to structure our evaluation heuristics. We also produce a pilot series of authentic scenarios designed to implicate the many dimensions of real-world human rights issues and annotated by human rights lawyers and professionals across the world. Ultimately, we find that model accuracy scores range considerably across legal reasoning tasks (overall model performance ranges from 0.339 to 0.577, task min-max ranges from 0.025 to 0.774), which strongly implies that HumRightsBench is a capable instrument for advancing this emerging subfield of AI evaluations science at a critical moment in its evolution.
| Comments: | Best paper, honorable mention, at the ICML 2026 Workshop on AI4Law, Seoul, South Korea |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computers and Society (cs.CY) |
| Cite as: | arXiv:2608.10268 [cs.LG] |
| (or arXiv:2608.10268v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2608.10268
arXiv-issued DOI via DataCite (pending registration)
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Submission history
From: Wm. Matthew Kennedy [view email][v1] Mon, 10 Aug 2026 22:09:05 UTC (3,209 KB)
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