Artificial intelligence harm and accountability by businesses: A systematic literature review
Dzandu, Michael; Asiedu, Sylvester; Pathak, Buddhi; De Cesare, Sergio
Authors
Michael Dzandu
Sylvester Asiedu
Buddhi Pathak
Sergio De Cesare
Abstract
This study reviews the literature on artificial intelligence (AI) harms caused by businesses, their impact on stakeholders, and the available remedial mechanisms. Using the PRISMA method, relevant articles were sourced from the Scopus database and critically analysed. The data revealed that only 38 articles were published on the topic between 2012 and 2024, with 21 of these in 2024 alone. Key AI harms identified include economic and employment displacement, user harm, bias and discrimination, the digital divide, and environmental harm. While an explicit AI harm accountability framework was not found, related frameworks were derived from six cognate areas: data governance, decision-making, ethical AI, legal frameworks, responsible AI, and AI implementation. Five themes—AI transparency, accountability, decision-making, ethics, and risk—emerged as central to the literature. The study concludes that accountability for AI harms by businesses has been an afterthought relative to the rapid adoption of AI during the review period.
Developing a robust AI accountability framework to guide businesses in mitigating AI harm is therefore imperative.
Presentation Conference Type
Conference Paper (unpublished)
Conference Name
27th International Conference on Enterprise Information Systems, ICEIS 2025
