This study offers an in-depth analysis of the application and implications of the National Institute of Standards and Technology's AI Risk Management Framework (NIST AI RMF) within the domain of surveillance technologies, particularly facial recognition technology. Given the inherently high-risk and consequential nature of facial recognition systems, our research emphasizes the critical need for a structured approach to risk management in this sector. The paper presents a detailed case study demonstrating the utility of the NIST AI RMF in identifying and mitigating risks that might otherwise remain unnoticed in these technologies. Our primary objective is to develop a comprehensive risk management strategy that advances the practice of responsible AI utilization in feasible, scalable ways. We propose a six-step process tailored to the specific challenges of surveillance technology that aims to produce a more systematic and effective risk management practice. This process emphasizes continual assessment and improvement to facilitate companies in managing AI-related risks more robustly and ensuring ethical and responsible deployment of AI systems. Additionally, our analysis uncovers and discusses critical gaps in the current framework of the NIST AI RMF, particularly concerning its application to surveillance technologies. These insights contribute to the evolving discourse on AI governance and risk management, highlighting areas for future refinement and development in frameworks like the NIST AI RMF.

翻译：本研究深入分析了美国国家标准与技术研究院人工智能风险管理框架（NIST AI RMF）在监控技术领域，特别是人脸识别技术中的应用与启示。鉴于人脸识别系统固有的高风险性和重大影响，我们的研究强调在该领域采用结构化风险管理方法的必要性。本文通过详细案例研究，展示了NIST AI RMF在识别和缓解这些技术中可能被忽视的风险方面的实用性。我们的主要目标是制定一套可行的、可扩展的全面风险管理策略，以推动负责任AI应用实践的发展。我们提出了一套针对监控技术特有挑战的六步流程，旨在形成更系统、更有效的风险管理实践。该流程强调持续评估与改进，以帮助企业更稳健地管理AI相关风险，并确保AI系统的道德与负责任部署。此外，我们的分析揭示并讨论了NIST AI RMF现有框架中的关键缺口，特别是在监控技术应用方面的不足。这些见解为AI治理与风险管理的持续讨论贡献了力量，指明了NIST AI RMF等框架未来完善与发展的方向。