An AI ethics ‘David and Goliath’

Value conflicts between large tech companies and their employees

Bibliographic Data

ID20397296
AuthorsMark Ryan (0000-0003-4850-0111, Wageningen University & Research, corresponding author), Eleni Christodoulou (0000-0001-8452-9312, University of Cyprus), Josephina Antoniou (0000-0003-0169-1299, University of Central Lancashire Cyprus), Kalypso Iordanou (0000-0001-5930-9393, University of Central Lancashire Cyprus)
Year2024
Volume39
Issue2
Pages557-572
Publication date2024-04-01
Open AccessYes
TypeARTICLE
VenueAI & Society (JOURNAL)
Journal identifiersISSN: 0951-5666 • E-ISSN: 1435-5655
PublisherSpringer Science and Business Media LLC (PUBLISHER)
DOI10.1007/s00146-022-01430-1
OpenAlexW4220689410
LanguageEN
Citations received4
References cited28

Artificial intelligence ethics requires a united approach from policymakers, AI companies, and individuals, in the development, deployment, and use of these technologies. However, sometimes discussions can become fragmented because of the different levels of governance (Schmitt in AI Ethics 1–12, 2021) or because of different values, stakeholders, and actors involved (Ryan and Stahl in J Inf Commun Ethics Soc 19:61–86, 2021). Recently, these conflicts became very visible, with such examples as the dismissal of AI ethics researcher Dr. Timnit Gebru from Google and the resignation of whistle-blower Frances Haugen from Facebook. Underpinning each debacle was a conflict between the organisation’s economic and business interests and the morals of their employees. This paper will examine tensions between the ethics of AI organisations and the values of their employees, by providing an exploration of the AI ethics literature in this area, and a qualitative analysis of three workshops with AI developers and practitioners. Common ethical and social tensions (such as power asymmetries, mistrust, societal risks, harms, and lack of transparency) will be discussed, along with proposals on how to avoid or reduce these conflicts in practice (e.g., building trust, fair allocation of responsibility, protecting employees’ autonomy, and encouraging ethical training and practice). Altogether, we suggest the following steps to help reduce ethical issues within AI organisations: improved and diverse ethics education and training within businesses; internal and external ethics auditing; the establishment of AI ethics ombudsmen, AI ethics review committees and an AI ethics watchdog; as well as access to trustworthy AI ethics whistle-blower organisations

Applied ethics · Audit · Autonomy · Business ethics · Corporate governance · Economics · Engineering ethics · Ethics of technology · Information ethics · Management · Meta-ethics · Political science · Public relations · Sociology · Blockchain Technology Applications and Security · Engineering · Ethics and Social Impacts of AI · Law · Psychology of Moral and Emotional Judgment

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