TY - JOUR
T1 - A systematic review of artificial intelligence impact assessments
AU - Stahl, Bernd Carsten
AU - Antoniou, Josephina
AU - Bhalla, Nitika
AU - Brooks, Laurence
AU - Jansen, Philip
AU - Lindqvist, Blerta
AU - Kirichenko, Alexey
AU - Marchal, Samuel
AU - Rodrigues, Rowena
AU - Santiago, Nicole
AU - Warso, Zuzanna
AU - Wright, David
N1 - Publisher Copyright:
© 2023, The Author(s).
PY - 2023/11
Y1 - 2023/11
N2 - Artificial intelligence (AI) is producing highly beneficial impacts in many domains, from transport to healthcare, from energy distribution to marketing, but it also raises concerns about undesirable ethical and social consequences. AI impact assessments (AI-IAs) are a way of identifying positive and negative impacts early on to safeguard AI’s benefits and avoid its downsides. This article describes the first systematic review of these AI-IAs. Working with a population of 181 documents, the authors identified 38 actual AI-IAs and subjected them to a rigorous qualitative analysis with regard to their purpose, scope, organisational context, expected issues, timeframe, process and methods, transparency and challenges. The review demonstrates some convergence between AI-IAs. It also shows that the field is not yet at the point of full agreement on content, structure and implementation. The article suggests that AI-IAs are best understood as means to stimulate reflection and discussion concerning the social and ethical consequences of AI ecosystems. Based on the analysis of existing AI-IAs, the authors describe a baseline process of implementing AI-IAs that can be implemented by AI developers and vendors and that can be used as a critical yardstick by regulators and external observers to evaluate organisations’ approaches to AI.
AB - Artificial intelligence (AI) is producing highly beneficial impacts in many domains, from transport to healthcare, from energy distribution to marketing, but it also raises concerns about undesirable ethical and social consequences. AI impact assessments (AI-IAs) are a way of identifying positive and negative impacts early on to safeguard AI’s benefits and avoid its downsides. This article describes the first systematic review of these AI-IAs. Working with a population of 181 documents, the authors identified 38 actual AI-IAs and subjected them to a rigorous qualitative analysis with regard to their purpose, scope, organisational context, expected issues, timeframe, process and methods, transparency and challenges. The review demonstrates some convergence between AI-IAs. It also shows that the field is not yet at the point of full agreement on content, structure and implementation. The article suggests that AI-IAs are best understood as means to stimulate reflection and discussion concerning the social and ethical consequences of AI ecosystems. Based on the analysis of existing AI-IAs, the authors describe a baseline process of implementing AI-IAs that can be implemented by AI developers and vendors and that can be used as a critical yardstick by regulators and external observers to evaluate organisations’ approaches to AI.
KW - AI
KW - AI governance
KW - Impact assessment
KW - Systematic review
UR - http://www.scopus.com/inward/record.url?scp=85150626697&partnerID=8YFLogxK
U2 - 10.1007/s10462-023-10420-8
DO - 10.1007/s10462-023-10420-8
M3 - Article
AN - SCOPUS:85150626697
SN - 0269-2821
VL - 56
SP - 12799
EP - 12831
JO - Artificial intelligence review
JF - Artificial intelligence review
IS - 11
ER -