[2602.13292] Mirror: A Multi-Agent System for AI-Assisted Ethics Review

[2602.13292] Mirror: A Multi-Agent System for AI-Assisted Ethics Review

arXiv - AI 4 min read Article

Summary

The paper presents Mirror, a multi-agent system designed to enhance AI-assisted ethics reviews, addressing the limitations of current ethical oversight mechanisms in research.

Why It Matters

As interdisciplinary scientific practices grow, the demand for effective ethics review systems increases. Mirror leverages advancements in AI to improve ethical decision-making processes, ensuring compliance and enhancing the quality of assessments in research governance.

Key Takeaways

  • Mirror integrates ethical reasoning and multi-agent deliberation for improved ethics reviews.
  • The system operates in two modes: expedited review and committee review.
  • Empirical evaluations show Mirror outperforms generalist LLMs in ethics assessment quality.

Computer Science > Artificial Intelligence arXiv:2602.13292 (cs) [Submitted on 9 Feb 2026] Title:Mirror: A Multi-Agent System for AI-Assisted Ethics Review Authors:Yifan Ding, Yuhui Shi, Zhiyan Li, Zilong Wang, Yifeng Gao, Yajun Yang, Mengjie Yang, Yixiu Liang, Xipeng Qiu, Xuanjing Huang, Xingjun Ma, Yu-Gang Jiang, Guoyu Wang View a PDF of the paper titled Mirror: A Multi-Agent System for AI-Assisted Ethics Review, by Yifan Ding and 12 other authors View PDF HTML (experimental) Abstract:Ethics review is a foundational mechanism of modern research governance, yet contemporary systems face increasing strain as ethical risks arise as structural consequences of large-scale, interdisciplinary scientific practice. The demand for consistent and defensible decisions under heterogeneous risk profiles exposes limitations in institutional review capacity rather than in the legitimacy of ethics oversight. Recent advances in large language models (LLMs) offer new opportunities to support ethics review, but their direct application remains limited by insufficient ethical reasoning capability, weak integration with regulatory structures, and strict privacy constraints on authentic review materials. In this work, we introduce Mirror, an agentic framework for AI-assisted ethical review that integrates ethical reasoning, structured rule interpretation, and multi-agent deliberation within a unified architecture. At its core is EthicsLLM, a foundational model fine-tuned on EthicsQA, a special...

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