Both eyes open: Vigilant Incentives help Regulatory Markets improve AI Safety

• URL: http://arxiv.org/abs/2303.03174v1
• Date: Mon, 6 Mar 2023 14:42:05 GMT
• Title: Both eyes open: Vigilant Incentives help Regulatory Markets improve AI Safety
• Authors: Paolo Bova and Alessandro Di Stefano and The Anh Han
• Abstract summary: Regulatory Markets for AI is a proposal designed with adaptability in mind. It involves governments setting outcome-based targets for AI companies to achieve. We warn that it is alarmingly easy to stumble on incentives which would prevent Regulatory Markets from achieving this goal.
• Score: 69.59465535312815
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the context of rapid discoveries by leaders in AI, governments must consider how to design regulation that matches the increasing pace of new AI capabilities. Regulatory Markets for AI is a proposal designed with adaptability in mind. It involves governments setting outcome-based targets for AI companies to achieve, which they can show by purchasing services from a market of private regulators. We use an evolutionary game theory model to explore the role governments can play in building a Regulatory Market for AI systems that deters reckless behaviour. We warn that it is alarmingly easy to stumble on incentives which would prevent Regulatory Markets from achieving this goal. These 'Bounty Incentives' only reward private regulators for catching unsafe behaviour. We argue that AI companies will likely learn to tailor their behaviour to how much effort regulators invest, discouraging regulators from innovating. Instead, we recommend that governments always reward regulators, except when they find that those regulators failed to detect unsafe behaviour that they should have. These 'Vigilant Incentives' could encourage private regulators to find innovative ways to evaluate cutting-edge AI systems.

Related papers

• Using AI Alignment Theory to understand the potential pitfalls of regulatory frameworks [55.2480439325792]
  This paper critically examines the European Union's Artificial Intelligence Act (EU AI Act) Uses insights from Alignment Theory (AT) research, which focuses on the potential pitfalls of technical alignment in Artificial Intelligence. As we apply these concepts to the EU AI Act, we uncover potential vulnerabilities and areas for improvement in the regulation.
  arXiv  Detail & Related papers  (2024-10-10T17:38:38Z)
• An FDA for AI? Pitfalls and Plausibility of Approval Regulation for Frontier Artificial Intelligence [0.0]
  We explore the applicability of approval regulation -- that is, regulation of a product that combines experimental minima with government licensure conditioned partially or fully upon that experimentation -- to the regulation of frontier AI. There are a number of reasons to believe that approval regulation, simplistically applied, would be inapposite for frontier AI risks. We conclude by highlighting the role of policy learning and experimentation in regulatory development.
  arXiv  Detail & Related papers  (2024-08-01T17:54:57Z)
• AI Procurement Checklists: Revisiting Implementation in the Age of AI Governance [18.290959557311552]
  Public sector use of AI has been on the rise for the past decade, but only recently have efforts to enter it entered the cultural zeitgeist. While simple to articulate, promoting ethical and effective roll outs of AI systems in government is a notoriously elusive task.
  arXiv  Detail & Related papers  (2024-04-23T01:45:38Z)
• Trust AI Regulation? Discerning users are vital to build trust and effective AI regulation [32.98110040460262]
  We show that creating trustworthy AI and user trust requires regulators to be incentivised to regulate effectively. We demonstrate the effectiveness of two mechanisms that can achieve this. We then consider an alternative solution, where users can condition their trust decision on the effectiveness of the regulators.
  arXiv  Detail & Related papers  (2024-03-14T15:56:39Z)
• 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)
• 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)
• Regulatory Markets: The Future of AI Governance [0.7230697742559377]
  Overreliance on industry self-regulation fails to hold producers and users accountable to democratic demands. This approach to AI regulation could overcome the limitations of both command-and-control regulation and self-regulation.
  arXiv  Detail & Related papers  (2023-04-11T01:05:55Z)
• Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims [59.64274607533249]
  AI developers need to make verifiable claims to which they can be held accountable. This report suggests various steps that different stakeholders can take to improve the verifiability of claims made about AI systems. We analyze ten mechanisms for this purpose--spanning institutions, software, and hardware--and make recommendations aimed at implementing, exploring, or improving those mechanisms.
  arXiv  Detail & Related papers  (2020-04-15T17:15:35Z)

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.