Three lines of defense against risks from AI

• URL: http://arxiv.org/abs/2212.08364v1
• Date: Fri, 16 Dec 2022 09:33:00 GMT
• Title: Three lines of defense against risks from AI
• Authors: Jonas Schuett
• Abstract summary: It is not always clear who is responsible for AI risk management. The Three Lines of Defense (3LoD) model is considered best practice in many industries. I suggest ways in which AI companies could implement the model.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Organizations that develop and deploy artificial intelligence (AI) systems need to manage the associated risks - for economic, legal, and ethical reasons. However, it is not always clear who is responsible for AI risk management. The Three Lines of Defense (3LoD) model, which is considered best practice in many industries, might offer a solution. It is a risk management framework that helps organizations to assign and coordinate risk management roles and responsibilities. In this article, I suggest ways in which AI companies could implement the model. I also discuss how the model could help reduce risks from AI: it could identify and close gaps in risk coverage, increase the effectiveness of risk management practices, and enable the board of directors to oversee management more effectively. The article is intended to inform decision-makers at leading AI companies, regulators, and standard-setting bodies.

Related papers

• Engineering Trustworthy AI: A Developer Guide for Empirical Risk Minimization [53.80919781981027]
  Key requirements for trustworthy AI can be translated into design choices for the components of empirical risk minimization. We hope to provide actionable guidance for building AI systems that meet emerging standards for trustworthiness of AI.
  arXiv  Detail & Related papers  (2024-10-25T07:53:32Z)
• Risk Alignment in Agentic AI Systems [0.0]
  Agentic AIs capable of undertaking complex actions with little supervision raise new questions about how to safely create and align such systems with users, developers, and society. Risk alignment will matter for user satisfaction and trust, but it will also have important ramifications for society more broadly. We present three papers that bear on key normative and technical aspects of these questions.
  arXiv  Detail & Related papers  (2024-10-02T18:21:08Z)
• Risks and Opportunities of Open-Source Generative AI [64.86989162783648]
  Applications of Generative AI (Gen AI) are expected to revolutionize a number of different areas, ranging from science & medicine to education. The potential for these seismic changes has triggered a lively debate about the potential risks of the technology, and resulted in calls for tighter regulation. This regulation is likely to put at risk the budding field of open-source generative AI.
  arXiv  Detail & Related papers  (2024-05-14T13:37:36Z)
• Near to Mid-term Risks and Opportunities of Open-Source Generative AI [94.06233419171016]
  Applications of Generative AI are expected to revolutionize a number of different areas, ranging from science & medicine to education. The potential for these seismic changes has triggered a lively debate about potential risks and resulted in calls for tighter regulation. This regulation is likely to put at risk the budding field of open-source Generative AI.
  arXiv  Detail & Related papers  (2024-04-25T21:14:24Z)
• 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)
• The risks of risk-based AI regulation: taking liability seriously [46.90451304069951]
  The development and regulation of AI seems to have reached a critical stage. Some experts are calling for a moratorium on the training of AI systems more powerful than GPT-4. This paper analyses the most advanced legal proposal, the European Union's AI Act.
  arXiv  Detail & Related papers  (2023-11-03T12:51:37Z)
• 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)
• An Overview of Catastrophic AI Risks [38.84933208563934]
  This paper provides an overview of the main sources of catastrophic AI risks, which we organize into four categories. Malicious use, in which individuals or groups intentionally use AIs to cause harm; AI race, in which competitive environments compel actors to deploy unsafe AIs or cede control to AIs. organizational risks, highlighting how human factors and complex systems can increase the chances of catastrophic accidents. rogue AIs, describing the inherent difficulty in controlling agents far more intelligent than humans.
  arXiv  Detail & Related papers  (2023-06-21T03:35:06Z)
• 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)
• 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.