A systematic review of artificial intelligence impact assessments

Funding Information: The authors would like to acknowledge the contribution of colleagues from the SHERPA project as well as other researchers with whom this work was discussed. This article draws from research undertaken by the EU-funded projects SHERPA ( www.project-sherpa.eu ) and the Human Brain Project SGA3 ( www.humanbrainproject ) that have received funding from the European Union’s Horizon 2020 Framework Programme for Research and Innovation under Grant Nos. 786641 and 945539. This work was supported by the UK Engineering and Physical Sciences Research Council [Horizon Digital Economy Research ‘Trusted Data Driven Products: EP/T022493/1] | openaire: EC/H2020/786641/EU//SHERPA | openaire: EC/H2020/945539/EU//HBP SGA3 ; 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 ...