Abstract
Abstract
Human oversight has become a key mechanism for the governance of artificial intelligence (“AI”). Human overseers are supposed to increase the accuracy and safety of AI systems, uphold human values, and build trust in the technology. Empirical research suggests, however, that humans are not reliable in fulfilling their oversight tasks. They may be lacking in competence or be harmfully incentivised. This creates a challenge for human oversight to be effective. In addressing this challenge, this article aims to make three contributions. First, it surveys the emerging laws of oversight, most importantly the European Union’s Artificial Intelligence Act (“AIA”). It will be shown that while the AIA is concerned with the competence of human overseers, it does not provide much guidance on how to achieve effective oversight and leaves oversight obligations for AI developers underdefined. Second, this article presents a novel taxonomy of human oversight roles, differentiated along whether human intervention is constitutive to, or corrective of a decision made or supported by an AI. The taxonomy allows to propose suggestions for improving effectiveness tailored to the type of oversight in question. Third, drawing on scholarship within democratic theory, this article formulates six normative principles which institutionalise distrust in human oversight of AI. The institutionalisation of distrust has historically been practised in democratic governance. Applied for the first time to AI governance, the principles anticipate the fallibility of human overseers and seek to mitigate them at the level of institutional design. They aim to directly increase the trustworthiness of human oversight and to indirectly inspire well-placed trust in AI governance.
Funder
British Academy
Department of Health and Social Care
Alfred P. Sloan Foundation
Wellcome Trust
Luminate Group
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Human-Computer Interaction,Philosophy
Reference75 articles.
1. Agrawal A, Gans J, Goldfarb A (2022) Power and prediction: the disruptive economics of artificial intelligence. Harvard Business Review Press, Boston, M.A.
2. Andrade NNG de and Zarra A (2022) Artificial intelligence act: a policy prototyping experiment: operationalizing the requirements for AI systems—Part I. https://openloop.org/reports/2022/11/Artificial_Intelligence_Act_A_Policy_Prototyping_Experiment_Operationalizing_Reqs_Part1.pdf.
3. Angwin J, Larson J, Mattu S, et al. (2016) Machine Bias. In: ProPublica. https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing. Accessed 8 Jan 2023.
4. Aoki N (2021) The importance of the assurance that “humans are still in the decision loop” for public trust in artificial intelligence: Evidence from an online experiment. Computers in Human Behavior 114. Elsevier Ltd. https://doi.org/10.1016/j.chb.2020.106572.
5. Article 29 Data Protection Working Party (2017) Guidelines on Automated individual decision-making and Profiling for the purposes of Regulation 2016/679. 17/EN WP 251, 3 October.
Cited by
3 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献