Towards Human-Centered RegTech: Unpacking Professionals' Strategies and Needs for Using LLMs Safely

2025-10-01

AI Summary

The deployment of large language models (LLMs) in high-stakes domains—such as law, healthcare, and finance—introduces underexplored compliance risks, including sensitive information leakage, intellectual property infringement, and uncontrolled outputs; existing NLP tools lack domain-specific compliance adaptation. Method: Through semi-structured interviews and qualitative analysis with frontline domain experts, we systematically identify real-world risk perceptions and emergent mitigation strategies, uncovering a structural misalignment between current tools and human-centered compliance requirements. Contribution/Results: We propose a human-centered RegTech compliance design framework for LLM-based NLP systems, centered on three core mechanisms: sensitive data protection, intellectual property security, and output quality controllability. This framework provides empirically grounded, actionable design principles to support the development of compliance-embedded NLP systems.

Technology Category

• Natural Language Processing: Safety and Robustness
• Machine Learning: Large Multimodal Models (LMMs)
• Humans and AI: AI for Accessibility

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

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.

Problem

• Addressing severe compliance risks in LLM usage across professional domains
• Investigating professionals' spontaneous mitigation strategies for LLM security concerns
• Bridging the gap between NLP tools and actual compliance needs

Innovation

• Human-centered compliance-driven NLP system design
• Proactive support for expert compliance workflows
• Empirical foundation for Regulatory Technology development