Governing Through the Cloud: The Intermediary Role of Compute Providers in AI Regulation
- URL: http://arxiv.org/abs/2403.08501v2
- Date: Tue, 26 Mar 2024 06:23:30 GMT
- Title: Governing Through the Cloud: The Intermediary Role of Compute Providers in AI Regulation
- Authors: Lennart Heim, Tim Fist, Janet Egan, Sihao Huang, Stephen Zekany, Robert Trager, Michael A Osborne, Noa Zilberman,
- Abstract summary: We argue that compute providers should have legal obligations and ethical responsibilities associated with AI development and deployment.
Compute providers can play an essential role in a regulatory ecosystem via four key capacities.
- Score: 14.704747149179047
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: As jurisdictions around the world take their first steps toward regulating the most powerful AI systems, such as the EU AI Act and the US Executive Order 14110, there is a growing need for effective enforcement mechanisms that can verify compliance and respond to violations. We argue that compute providers should have legal obligations and ethical responsibilities associated with AI development and deployment, both to provide secure infrastructure and to serve as intermediaries for AI regulation. Compute providers can play an essential role in a regulatory ecosystem via four key capacities: as securers, safeguarding AI systems and critical infrastructure; as record keepers, enhancing visibility for policymakers; as verifiers of customer activities, ensuring oversight; and as enforcers, taking actions against rule violations. We analyze the technical feasibility of performing these functions in a targeted and privacy-conscious manner and present a range of technical instruments. In particular, we describe how non-confidential information, to which compute providers largely already have access, can provide two key governance-relevant properties of a computational workload: its type-e.g., large-scale training or inference-and the amount of compute it has consumed. Using AI Executive Order 14110 as a case study, we outline how the US is beginning to implement record keeping requirements for compute providers. We also explore how verification and enforcement roles could be added to establish a comprehensive AI compute oversight scheme. We argue that internationalization will be key to effective implementation, and highlight the critical challenge of balancing confidentiality and privacy with risk mitigation as the role of compute providers in AI regulation expands.
Related papers
- 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) - AI Cards: Towards an Applied Framework for Machine-Readable AI and Risk Documentation Inspired by the EU AI Act [2.1897070577406734]
Despite its importance, there is a lack of standards and guidelines to assist with drawing up AI and risk documentation aligned with the AI Act.
We propose AI Cards as a novel holistic framework for representing a given intended use of an AI system.
arXiv Detail & Related papers (2024-06-26T09:51:49Z) - Computing Power and the Governance of Artificial Intelligence [51.967584623262674]
Governments and companies have started to leverage compute as a means to govern AI.
compute-based policies and technologies have the potential to assist in these areas, but there is significant variation in their readiness for implementation.
naive or poorly scoped approaches to compute governance carry significant risks in areas like privacy, economic impacts, and centralization of power.
arXiv Detail & Related papers (2024-02-13T21:10:21Z) - Visibility into AI Agents [9.067567737098594]
Increased delegation of commercial, scientific, governmental, and personal activities to AI agents may exacerbate existing societal risks.
We assess three categories of measures to increase visibility into AI agents: agent identifiers, real-time monitoring, and activity logging.
arXiv Detail & Related papers (2024-01-23T23:18:33Z) - 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) - 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) - Functional requirements to mitigate the Risk of Harm to Patients from
Artificial Intelligence in Healthcare [0.0]
This study proposes 14 functional requirements that AI systems may implement to reduce the risks associated with their medical purpose.
Our intention here is to provide specific high-level specifications of technical solutions to ensure continuous good performance and use of AI systems to benefit patients in compliance with the future EU regulatory framework.
arXiv Detail & Related papers (2023-09-19T08:37:22Z) - An interdisciplinary conceptual study of Artificial Intelligence (AI)
for helping benefit-risk assessment practices: Towards a comprehensive
qualification matrix of AI programs and devices (pre-print 2020) [55.41644538483948]
This paper proposes a comprehensive analysis of existing concepts coming from different disciplines tackling the notion of intelligence.
The aim is to identify shared notions or discrepancies to consider for qualifying AI systems.
arXiv Detail & Related papers (2021-05-07T12:01:31Z) - Trustworthy AI Inference Systems: An Industry Research View [58.000323504158054]
We provide an industry research view for approaching the design, deployment, and operation of trustworthy AI inference systems.
We highlight opportunities and challenges in AI systems using trusted execution environments.
We outline areas of further development that require the global collective attention of industry, academia, and government researchers.
arXiv Detail & Related papers (2020-08-10T23:05:55Z) - 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.