arXiv cs.CY· Drishti Goel, Jeongah Lee, Qiuyue Joy Zhong, Violeta J. Rodriguez, Daniel S. Brown, Ravi Karkar, Dong Whi Yoo, Koustuv Saha·· 13 小时前AI 评分38
RubRIX：面向看护者-AI 交互的评分标准驱动风险缓解框架
RubRIX: Rubric-Driven Risk Mitigation in Caregiver-AI Interactions
AI 导读
研究者提出 RubRIX（Rubric-based Risk Index），一个基于关怀伦理、经临床医生验证的 LLM 看护回复风险评估框架，将风险拆为疏忽、偏见与污名、信息不准确、无批判肯定、认知傲慢五个维度。
正文 · 原文
Abstract:Caregivers seeking AI-mediated support express complex needs---information-seeking, emotional validation, and distress cues---that warrant careful evaluation of response safety and appropriateness. Existing AI evaluation frameworks, primarily focused on general risks (toxicity, hallucinations, policy violations, etc), may not adequately capture the nuanced risks of LLM-responses in caregiving-contexts. We introduce RubRIX (Rubric-based Risk Index), a theory-driven, clinician-validated framework for evaluating risks in LLM caregiving responses. Grounded in the Elements of an Ethic of Care, RubRIX operationalizes five empirically-derived risk dimensions: Inattention, Bias & Stigma, Information Inaccuracy, Uncritical Affirmation, and Epistemic Arrogance. We evaluate six state-of-the-art LLMs on over 20,000 caregiver queries from Reddit and ALZConnected. Rubric-guided refinement consistently reduced risk-components by 45-98% after one iteration across models. This work contributes a methodological approach for developing domain-sensitive, user-centered evaluation frameworks for high-burden contexts. Our findings highlight the importance of domain-sensitive, interactional risk evaluation for the responsible deployment of LLMs in caregiving support contexts. We release benchmark datasets to enable future research on contextual risk evaluation in AI-mediated support.
| Subjects: | Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computers and Society (cs.CY); Machine Learning (cs.LG); Social and Information Networks (cs.SI) |
| Cite as: | arXiv:2601.13235 [cs.HC] |
| (or arXiv:2601.13235v2 [cs.HC] for this version) | |
| https://doi.org/10.48550/arXiv.2601.13235 arXiv-issued DOI via DataCite |
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| Journal reference: | Findings of the Association for Computational Linguistics: ACL 2026 |
| Related DOI: | https://doi.org/10.18653/v1/2026.findings-acl.1774
DOI(s) linking to related resources |
Submission history
From: Koustuv Saha [view email]
[v1]
Mon, 19 Jan 2026 17:10:49 UTC (709 KB)
[v2]
Wed, 21 Oct 2026 15:26:27 UTC (347 KB)
来源：arXiv cs.CY · arxiv.org