Application of the NIST AI Risk Management Framework to Surveillance Technology

• URL: http://arxiv.org/abs/2403.15646v1
• Date: Fri, 22 Mar 2024 23:07:11 GMT
• Title: Application of the NIST AI Risk Management Framework to Surveillance Technology
• Authors: Nandhini Swaminathan, David Danks,
• Abstract summary: 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) 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.
• Score: 1.5442389863546546
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• SoK: Unifying Cybersecurity and Cybersafety of Multimodal Foundation Models with an Information Theory Approach [58.93030774141753]
  Multimodal foundation models (MFMs) represent a significant advancement in artificial intelligence. This paper conceptualizes cybersafety and cybersecurity in the context of multimodal learning. We present a comprehensive Systematization of Knowledge (SoK) to unify these concepts in MFMs, identifying key threats.
  arXiv  Detail & Related papers  (2024-11-17T23:06:20Z)
• EAIRiskBench: Towards Evaluating Physical Risk Awareness for Task Planning of Foundation Model-based Embodied AI Agents [47.69642609574771]
  Embodied artificial intelligence (EAI) integrates advanced AI models into physical entities for real-world interaction. Foundation models as the "brain" of EAI agents for high-level task planning have shown promising results. However, the deployment of these agents in physical environments presents significant safety challenges. This study introduces EAIRiskBench, a novel framework for automated physical risk assessment in EAI scenarios.
  arXiv  Detail & Related papers  (2024-08-08T13:19:37Z)
• Open Problems in Technical AI Governance [93.89102632003996]
  Technical AI governance refers to technical analysis and tools for supporting the effective governance of AI. This paper is intended as a resource for technical researchers or research funders looking to contribute to AI governance.
  arXiv  Detail & Related papers  (2024-07-20T21:13:56Z)
• Approaching Emergent Risks: An Exploratory Study into Artificial Intelligence Risk Management within Financial Organisations [0.0]
  This study aims to contribute to the understanding of AI risk management in organisations through an exploratory empirical investigation into these practices. In-depth insights are gained through interviews with nine practitioners from different organisations within the UK financial sector. The findings of this study unearth levels of risk management framework readiness and prevailing approaches to risk management at both a processual and organisational level.
  arXiv  Detail & Related papers  (2024-04-08T20:28:22Z)
• Prioritizing Safeguarding Over Autonomy: Risks of LLM Agents for Science [65.77763092833348]
  Intelligent agents powered by large language models (LLMs) have demonstrated substantial promise in autonomously conducting experiments and facilitating scientific discoveries across various disciplines. While their capabilities are promising, these agents also introduce novel vulnerabilities that demand careful consideration for safety. This paper conducts a thorough examination of vulnerabilities in LLM-based agents within scientific domains, shedding light on potential risks associated with their misuse and emphasizing the need for safety measures.
  arXiv  Detail & Related papers  (2024-02-06T18:54:07Z)
• Control Risk for Potential Misuse of Artificial Intelligence in Science [85.91232985405554]
  We aim to raise awareness of the dangers of AI misuse in science. We highlight real-world examples of misuse in chemical science. We propose a system called SciGuard to control misuse risks for AI models in science.
  arXiv  Detail & Related papers  (2023-12-11T18:50:57Z)
• Managing extreme AI risks amid rapid progress [171.05448842016125]
  We describe risks that include large-scale social harms, malicious uses, and irreversible loss of human control over autonomous AI systems. There is a lack of consensus about how exactly such risks arise, and how to manage them. Present governance initiatives lack the mechanisms and institutions to prevent misuse and recklessness, and barely address autonomous systems.
  arXiv  Detail & Related papers  (2023-10-26T17:59:06Z)
• AI Hazard Management: A framework for the systematic management of root causes for AI risks [0.0]
  This paper introduces the AI Hazard Management (AIHM) framework. It provides a structured process to systematically identify, assess, and treat AI hazards. It builds upon an AI hazard list from a comprehensive state-of-the-art analysis.
  arXiv  Detail & Related papers  (2023-10-25T15:55:50Z)
• A Brief Overview of AI Governance for Responsible Machine Learning Systems [3.222802562733787]
  This position paper seeks to present a brief introduction to AI governance, which is a framework designed to oversee the responsible use of AI. Due to the probabilistic nature of AI, the risks associated with it are far greater than traditional technologies.
  arXiv  Detail & Related papers  (2022-11-21T23:48:51Z)
• Quantitative AI Risk Assessments: Opportunities and Challenges [9.262092738841979]
  AI-based systems are increasingly being leveraged to provide value to organizations, individuals, and society. Risks have led to proposed regulations, litigation, and general societal concerns. This paper explores the concept of a quantitative AI Risk Assessment.
  arXiv  Detail & Related papers  (2022-09-13T21:47:25Z)
• Actionable Guidance for High-Consequence AI Risk Management: Towards Standards Addressing AI Catastrophic Risks [12.927021288925099]
  Artificial intelligence (AI) systems can present risks of events with very high or catastrophic consequences at societal scale. NIST is developing the NIST Artificial Intelligence Risk Management Framework (AI RMF) as voluntary guidance on AI risk assessment and management. We provide detailed actionable-guidance recommendations focused on identifying and managing risks of events with very high or catastrophic consequences.
  arXiv  Detail & Related papers  (2022-06-17T18:40:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.