调研专业人士用大模型的合规风险与应对策略，为安全可靠的监管科技提供设计依据。
Towards Human-Centered RegTech: Unpacking Professionals' Strategies and Needs for Using LLMs Safely
- 通过访谈24位专业人士，发现其自发采用数据变形等方法规避风险
- 多数人担心信息泄露与输出质量不确定，但现有工具缺乏合规指导
- 提出人本导向的监管科技新方向，适合关注AI合规的开发者和政策制定者
大型语言模型正在深刻改变高风险专业领域的作业模式，但其应用也带来了严重且未被充分研究的合规风险。我们对法律、医疗、金融等行业24名高技能知识工作者进行了半结构化访谈。研究发现，这些专家普遍担忧敏感信息泄露、知识产权侵权以及模型输出质量的不确定性。为此，他们自发采取多种缓解策略，如主动扭曲输入数据、限制提示词细节。然而，由于缺乏针对大模型的明确合规指引和培训，这些自发努力的效果有限。研究揭示了当前NLP工具与专业人士实际合规需求之间的显著差距。本文将这些宝贵的实证发现定位为下一代以人为本、以合规为导向的自然语言处理在监管科技（RegTech）中发展的基础，为工程化设计能主动支持专家合规流程的NLP系统提供了关键的人本视角与设计要求。
原文摘要 · Abstract (English)
Large Language Models are profoundly changing work patterns in high-risk professional domains, yet their application also introduces severe and underexplored compliance risks. To investigate this issue, we conducted semi-structured interviews with 24 highly-skilled knowledge workers from industries such as law, healthcare, and finance. The study found that these experts are commonly concerned about sensitive information leakage, intellectual property infringement, and uncertainty regarding the quality of model outputs. In response, they spontaneously adopt various mitigation strategies, such as actively distorting input data and limiting the details in their prompts. However, the effectiveness of these spontaneous efforts is limited due to a lack of specific compliance guidance and training for Large Language Models. Our research reveals a significant gap between current NLP tools and the actual compliance needs of experts. This paper positions these valuable empirical findings as foundational work for building the next generation of Human-Centered, Compliance-Driven Natural Language Processing for Regulatory Technology (RegTech), providing a critical human-centered perspective and design requirements for engineering NLP systems that can proactively support expert compliance workflows.
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