Koshiyama, Adriano; Kazim, Emre; Treleaven, Philip; Rai, Pete; Szpruch, Lukasz; Pavey, Giles; Ahamat, Ghazi; Leutner, Franziska; Goebel, Randy; Knight, Andrew; Adams, Janet; Hitrova, Christina; Barnett, Jeremy; Nachev, Parashkev; Barber, David; Chamorro-Premuzic, Tomas; Klemmer, Konstantin; Gregorovic, Miro; Khan, Shakeel; Lomas, Elizabeth; Hilliard, Airlie and Chatterjee, Siddhant. 2024. Towards algorithm auditing: managing legal, ethical and technological risks of AI, ML and associated algorithms. Royal Society Open Science, 11(5), 230859. ISSN 2054-5703 [Article]

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Abstract or Description

­Business reliance on algorithms is becoming ubiquitous, and companies are increasingly concerned about their algorithms causing major financial or reputational damage. High-profile cases include Google’s AI algorithm for photo classification mistakenly labelling a black couple as gorillas in 2015 (Gebru 2020 In The Oxford handbook of ethics of AI, pp. 251–269), Microsoft’s AI chatbot Tay that spread racist, sexist and antisemitic speech on Twitter (now X) (Wolf et al. 2017 ACM Sigcas Comput. Soc. 47, 54–64 (doi:10.1145/3144592.3144598)), and Amazon’s AI recruiting tool being scrapped after showing bias against women. In response, governments are legislating and imposing bans, regulators fining companies and the judiciary discussing potentially making algorithms artificial ‘persons’ in law. As with financial audits, governments, business and society will require algorithm audits; formal assurance that algorithms are legal, ethical and safe. A new industry is envisaged: Auditing and Assurance of Algorithms (cf. data privacy), with the remit to professionalize and industrialize AI, ML and associated algorithms. The stakeholders range from those working on policy/regulation to industry practitioners and developers. We also anticipate the nature and scope of the auditing levels and framework presented will inform those interested in systems of governance and compliance with regulation/standards. Our goal in this article is to survey the key areas necessary to perform auditing and assurance and instigate the debate in this novel area of research and practice.

Item Type:	Article
Identification Number (DOI):	https://doi.org/10.1098/rsos.230859
Additional Information:	Funding: Ad.K. and P.T. would like to acknowledge Cisco Research Centre for their research grant (no. 2019-207109, 3696).
Data Access Statement:	This article has no additional data.
Keywords:	artificial intelligence, machine learning, explainability, auditing, bias, transparency
Departments, Centres and Research Units:	Institute of Management Studies
Dates:	Date Event 13 February 2024 Accepted 15 May 2024 Published
Item ID:	37107
Date Deposited:	20 Jun 2024 10:30
Last Modified:	20 Jun 2024 10:30
Peer Reviewed:	Yes, this version has been peer-reviewed.
URI:	https://research.gold.ac.uk/id/eprint/37107

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