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A sector-based approach to AI ethics: Understanding ethical issues of AI-related incidents within their sectoral context

Dafna Burema; Nicole Debowski-Weimann; Alexander von Janowski; Jil Grabowski; Mihai Maftei; Mattis Jacobs; Patrick van der Smagt; Djalel Benbouzid
In: Francesca Rossi; Sanmay Das; Jenny Davis; Kay Firth-Butterfield; Alex John (Hrsg.). AIES '23: Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society. AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (AIES-2023), August 8-10, Montreal, Canada, Pages 705-714, ISBN 979-8-4007-0231-0, The Association for Computing Machinery, New York, NY, 8/2023.


Acknowledging that society is made up of different sectors with their own rules and structures, this paper studies the relevance of a sector-specific perspective to AI ethics. Incidents with AI are studied in relation to five sectors (police, healthcare, education and academia, politics, automotive) using the AIAAIC repository. A total of 125 incidents are sampled and analyzed by conducting a qualitative content analysis on media reports. The results show that certain ethical principles are found breached across sectors: accuracy/reliability, bias/discrimination, transparency, surveillance/privacy, security. However, results also show that 1) some ethical issues (misinformation, safety, premise/intent) are sector specific, 2) the consequences and meaning of the same ethical issue is able to vary across sectors and 3) pre-existing sector-specific issues are reproduced with these ethical breaches. The paper concludes that general ethical principles are relevant to discuss across sectors, yet, a sector-based approach to AI ethics gives in-depth information on sector-specific structural issues.


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