Frontier AI Regulation: Managing Emerging Risks to Public Safety

• URL: http://arxiv.org/abs/2307.03718v4
• Date: Tue, 7 Nov 2023 19:01:53 GMT
• Title: Frontier AI Regulation: Managing Emerging Risks to Public Safety
• Authors: Markus Anderljung, Joslyn Barnhart, Anton Korinek, Jade Leung, Cullen O'Keefe, Jess Whittlestone, Shahar Avin, Miles Brundage, Justin Bullock, Duncan Cass-Beggs, Ben Chang, Tantum Collins, Tim Fist, Gillian Hadfield, Alan Hayes, Lewis Ho, Sara Hooker, Eric Horvitz, Noam Kolt, Jonas Schuett, Yonadav Shavit, Divya Siddarth, Robert Trager, Kevin Wolf
• Abstract summary: "Frontier AI" models could possess dangerous capabilities sufficient to pose severe risks to public safety. Industry self-regulation is an important first step. We propose an initial set of safety standards.
• Score: 15.85618115026625
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Advanced AI models hold the promise of tremendous benefits for humanity, but society needs to proactively manage the accompanying risks. In this paper, we focus on what we term "frontier AI" models: highly capable foundation models that could possess dangerous capabilities sufficient to pose severe risks to public safety. Frontier AI models pose a distinct regulatory challenge: dangerous capabilities can arise unexpectedly; it is difficult to robustly prevent a deployed model from being misused; and, it is difficult to stop a model's capabilities from proliferating broadly. To address these challenges, at least three building blocks for the regulation of frontier models are needed: (1) standard-setting processes to identify appropriate requirements for frontier AI developers, (2) registration and reporting requirements to provide regulators with visibility into frontier AI development processes, and (3) mechanisms to ensure compliance with safety standards for the development and deployment of frontier AI models. Industry self-regulation is an important first step. However, wider societal discussions and government intervention will be needed to create standards and to ensure compliance with them. We consider several options to this end, including granting enforcement powers to supervisory authorities and licensure regimes for frontier AI models. Finally, we propose an initial set of safety standards. These include conducting pre-deployment risk assessments; external scrutiny of model behavior; using risk assessments to inform deployment decisions; and monitoring and responding to new information about model capabilities and uses post-deployment. We hope this discussion contributes to the broader conversation on how to balance public safety risks and innovation benefits from advances at the frontier of AI development.

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)
• Auction-Based Regulation for Artificial Intelligence [28.86995747151915]
  We propose an auction-based regulatory mechanism to regulate AI safety. We provably guarantee that each participating agent's best strategy is to submit a model safer than a prescribed minimum-safety threshold. Empirical results show that our regulatory auction boosts safety and participation rates by 20% and 15% respectively.
  arXiv  Detail & Related papers  (2024-10-02T17:57:02Z)
• From Principles to Rules: A Regulatory Approach for Frontier AI [2.1764247401772705]
  Regulators may require frontier AI developers to adopt safety measures. The requirements could be formulated as high-level principles or specific rules. These regulatory approaches, known as 'principle-based' and 'rule-based' regulation, have complementary strengths and weaknesses.
  arXiv  Detail & Related papers  (2024-07-10T01:45:15Z)
• 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)
• Towards Guaranteed Safe AI: A Framework for Ensuring Robust and Reliable AI Systems [88.80306881112313]
  We will introduce and define a family of approaches to AI safety, which we will refer to as guaranteed safe (GS) AI. The core feature of these approaches is that they aim to produce AI systems which are equipped with high-assurance quantitative safety guarantees. We outline a number of approaches for creating each of these three core components, describe the main technical challenges, and suggest a number of potential solutions to them.
  arXiv  Detail & Related papers  (2024-05-10T17:38:32Z)
• A Safe Harbor for AI Evaluation and Red Teaming [124.89885800509505]
  Some researchers fear that conducting such research or releasing their findings will result in account suspensions or legal reprisal. We propose that major AI developers commit to providing a legal and technical safe harbor. We believe these commitments are a necessary step towards more inclusive and unimpeded community efforts to tackle the risks of generative AI.
  arXiv  Detail & Related papers  (2024-03-07T20:55:08Z)
• 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)
• Oversight for Frontier AI through a Know-Your-Customer Scheme for Compute Providers [0.8547032097715571]
  Know-Your-Customer (KYC) is a standard developed by the banking sector to identify and verify client identity. KYC could provide a mechanism for greater public oversight of frontier AI development and close loopholes in existing export controls. Unlike the strategy of limiting access to AI chip purchases, regulating the digital access to compute offers more precise controls.
  arXiv  Detail & Related papers  (2023-10-20T16:17:29Z)
• Deployment Corrections: An incident response framework for frontier AI models [0.0]
  This paper explores contingency plans for cases where pre-deployment risk management falls short. We describe a toolkit of deployment corrections that AI developers can use to respond to dangerous capabilities. We recommend frontier AI developers, standard-setting organizations, and regulators should collaborate to define a standardized industry-wide approach.
  arXiv  Detail & Related papers  (2023-09-30T10:07:39Z)
• Dual Governance: The intersection of centralized regulation and crowdsourced safety mechanisms for Generative AI [1.2691047660244335]
  Generative Artificial Intelligence (AI) has seen mainstream adoption lately, especially in the form of consumer-facing, open-ended, text and image generating models. The potential for generative AI to displace human creativity and livelihoods has also been under intense scrutiny. Existing and proposed centralized regulations by governments to rein in AI face criticisms such as not having sufficient clarity or uniformity. Decentralized protections via crowdsourced safety tools and mechanisms are a potential alternative.
  arXiv  Detail & Related papers  (2023-08-02T23:25:21Z)

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.