Risk Alignment in Agentic AI Systems

• URL: http://arxiv.org/abs/2410.01927v1
• Date: Wed, 2 Oct 2024 18:21:08 GMT
• Title: Risk Alignment in Agentic AI Systems
• Authors: Hayley Clatterbuck, Clinton Castro, Arvo Muñoz Morán,
• Abstract summary: 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Agentic AIs $-$ AIs that are capable and permitted to undertake complex actions with little supervision $-$ mark a new frontier in AI capabilities and raise new questions about how to safely create and align such systems with users, developers, and society. Because agents' actions are influenced by their attitudes toward risk, one key aspect of alignment concerns the risk profiles of agentic AIs. Risk alignment will matter for user satisfaction and trust, but it will also have important ramifications for society more broadly, especially as agentic AIs become more autonomous and are allowed to control key aspects of our lives. AIs with reckless attitudes toward risk (either because they are calibrated to reckless human users or are poorly designed) may pose significant threats. They might also open 'responsibility gaps' in which there is no agent who can be held accountable for harmful actions. What risk attitudes should guide an agentic AI's decision-making? How might we design AI systems that are calibrated to the risk attitudes of their users? What guardrails, if any, should be placed on the range of permissible risk attitudes? What are the ethical considerations involved when designing systems that make risky decisions on behalf of others? We present three papers that bear on key normative and technical aspects of these questions.

Related papers

• Fully Autonomous AI Agents Should Not be Developed [58.88624302082713]
  This paper argues that fully autonomous AI agents should not be developed. In support of this position, we build from prior scientific literature and current product marketing to delineate different AI agent levels. Our analysis reveals that risks to people increase with the autonomy of a system.
  arXiv  Detail & Related papers  (2025-02-04T19:00:06Z)
• Imagining and building wise machines: The centrality of AI metacognition [78.76893632793497]
  We argue that shortcomings stem from one overarching failure: AI systems lack wisdom. While AI research has focused on task-level strategies, metacognition is underdeveloped in AI systems. We propose that integrating metacognitive capabilities into AI systems is crucial for enhancing their robustness, explainability, cooperation, and safety.
  arXiv  Detail & Related papers  (2024-11-04T18:10:10Z)
• 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)
• 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)
• 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)
• Three lines of defense against risks from AI [0.0]
  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.
  arXiv  Detail & Related papers  (2022-12-16T09:33:00Z)
• 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)
• Cybertrust: From Explainable to Actionable and Interpretable AI (AI2) [58.981120701284816]
  Actionable and Interpretable AI (AI2) will incorporate explicit quantifications and visualizations of user confidence in AI recommendations. It will allow examining and testing of AI system predictions to establish a basis for trust in the systems' decision making.
  arXiv  Detail & Related papers  (2022-01-26T18:53:09Z)

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.