2020/08/11 by Xavier Ferrer, Tom van Nuenen, Jose M. Such +4 · 257 citations
Computer Science · Psychology · Social Sciences · #Artificial intelligence #Cognitive psychology #Computer science #Cross disciplinary #Data science #Discipline #Ethics and Social Impacts of AI #Explainable Artificial Intelligence (XAI) #Gender bias #Implicit bias #Law #Law, AI, and Intellectual Property #Perspective (graphical) #Political science #Psychology #Social discrimination #Social issues #Social psychology #Social science #Sociology #cs.CY #cs.LG #msc:68T01
paper · pdf · doi:10.1109/mts.2021.3056293
published in IEEE Technology and Society Magazine 40(2), 72-80 (Institute of Electrical and Electronics Engineers (IEEE))
arxiv created 2020/08/11 · crossref issued 2021/06/01 · crossref published 2021/06/01 · crossref published-print 2021/06/01 · openalex publication_date 2021/06/01 · crossref created 2021/06/03 · arxiv updated 2021/06/07 · crossref deposited 2022/05/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05 · crossref indexed 2026/08/05
Operating at a large scale and impacting large groups of people, automated systems can make consequential and sometimes contestable decisions. Automated decisions can impact a range of phenomena, from credit scores to insurance payouts to health evaluations. These forms of automation can become problematic when they place certain groups or people at a systematic disadvantage. These are cases of discrimination-which is legally defined as the unfair or unequal treatment of an individual (or group) based on certain protected characteristics (also known as protected attributes) such as income, education, gender, or ethnicity. When the unfair treatment is caused by automated decisions, usually taken by intelligent agents or other AI-based systems, the topic of digital discrimination arises. Digital discrimination is prevalent in a diverse range of fields, such as in risk assessment systems for policing and credit scores [1], [2].