Can effective international governance for artificial intelligence remain fragmented, or is there a need for a centralised international organisation for AI? We draw on the history of other international regimes to identify advantages and disadvantages in centralising AI governance. Some considerations, such as efficiency and political power, speak in favour of centralisation. Conversely, the risk of creating a slow and brittle institution speaks against it, as does the difficulty in securing participation while creating stringent rules. Other considerations depend on the specific design of a centralised institution. A well-designed body may be able to deter forum shopping and ensure policy coordination. However, forum shopping can be beneficial and a fragmented landscape of institutions can be self-organising. Centralisation entails trade-offs and the details matter. We conclude with two core recommendations. First, the outcome will depend on the exact design of a central institution. A well-designed centralised regime covering a set of coherent issues could be beneficial. But locking-in an inadequate structure may pose a fate worse than fragmentation. Second, for now fragmentation will likely persist. This should be closely monitored to see if it is self-organising or simply inadequate.
Challenges and Best Practices in Corporate AI Governance:Lessons from the Biopharmaceutical Industry
Oxford Internet Institute, University of Oxford（牛津大学牛津互联网研究院）；Center for Information Technology Policy, Princeton University（普林斯顿大学信息技术政策中心）；AstraZeneca（阿斯利康）；University of Bologna（博洛尼亚大学）
A federated architecture for sector-led AI governance: lessons from India
Department of Telecommunications, Ministry of Communications, Sanchar Bhawan, New Delhi, India（印度通信部电信局，新德里）；Defence Institute of Advanced Technology, Ministry of Defence, Girinagar, Pune, India（印度国防部国防高级技术研究所，浦那）
University of Oxford（牛津大学）；The Alan Turing Institute（阿兰·图灵研究所）；Cardiff University（卡迪夫大学）
In this commentary, we respond to a recent editorial letter by Professor Luciano Floridi entitled 'AI as a public service: Learning from Amsterdam and Helsinki'. Here, Floridi considers the positive impact of these municipal AI registers, which collect a limited number of algorithmic systems used by the city of Amsterdam and Helsinki. There are a number of assumptions about AI registers as a governance model for automated systems that we seek to question. Starting with recent attempts to normalize AI by decontextualizing and depoliticizing it, which is a fraught political project that encourag…
This chapter formulates seven lessons for preventing harm in artificial intelligence (AI) systems based on insights from the field of system safety for software-based automation in safety-critical domains. New applications of AI across societal domains and public organizations and infrastructures come with new hazards, which lead to new forms of harm, both grave and pernicious. The text addresses the lack of consensus for diagnosing and eliminating new AI system hazards. For decades, the field of system safety has dealt with accidents and harm in safety-critical systems governed by varying deg…
Much attention has focused on algorithmic audits and impact assessments to hold developers and users of algorithmic systems accountable. But existing algorithmic accountability policy approaches have neglected the lessons from non-algorithmic domains: notably, the importance of interventions that allow for the effective participation of third parties. Our paper synthesizes lessons from other fields on how to craft effective systems of external oversight for algorithmic deployments. First, we discuss the challenges of third party oversight in the current AI landscape. Second, we survey audit sy…
An International Consortium for Evaluations of Societal-Scale Risks from Advanced AI
Wichita State University（威奇托州立大学）；Transformative Futures Institute（变革性未来研究所）；Centre for the Study of Existential Risk, University of Cambridge（剑桥大学生存风险研究中心）；Mila（米拉研究所）；St. Thomas University, Miami（迈阿密圣托马斯大学）；Legal Priorities Project（法律优先项目）；University of St Andrews（圣安德鲁斯大学）；University of Oxford（牛津大学）；Universitat Politècnica de València（瓦伦西亚理工大学）；Leverhulme Centre for the Future of Intelligence, University of Cambridge（剑桥大学勒沃休姆智能未来中心）；University College London（伦敦大学学院）；Apollo Research（阿波罗研究所）；National Intelligence University, U.S.A.（美国国家情报大学）；SaferAI；Center for the Future Mind, Florida Atlantic University（佛罗里达大西洋大学未来思维中心）
