Bias in data‐driven artificial intelligence systems—An introductory survey

Dados Bibliográficos

ID23326136
AutoresEirini Ntoutsi (0000-0001-5729-1003, Leibniz University Hannover, autor correspondente), Pavlos Fafalios (0000-0003-2788-526X, Foundation for Research and Technology Hellas), Ujwal Gadiraju (0000-0002-6189-6539, Leibniz University Hannover), Vasileios Iosifidis (0000-0002-3005-4507, Leibniz University Hannover), Wolfgang Nejdl (0000-0003-3374-2193, Leibniz University Hannover), Maria‐Esther Vidal (TIB Leibniz Information Centre For Science and Tecnhnology Hannover Germany), María-Esther Vidal (0000-0003-1160-8727, Technische Informationsbibliothek (TIB)), Salvatore Ruggieri (0000-0002-1917-6087, University of Pisa), Franco Turini (0000-0001-6789-5476, University of Pisa), Papadopoulo (0000-0002-5441-7341, Information Technologies Institute), Emmanouil Krasanakis (0000-0002-3947-222X, Information Technologies Institute), Ioannis Kompatsiaris (0000-0001-6447-9020, Information Technologies Institute), Katharina Kinder-Kurlanda (0000-0002-7749-645X, GESIS - Leibniz Institute for the Social Sciences), Claudia Wagner (0000-0002-0640-8221, GESIS - Leibniz Institute for the Social Sciences), Fowzia Karimi (0000-0002-0037-2475, GESIS - Leibniz Institute for the Social Sciences), Fariba Karimi (0000-0002-2209-7590, GESIS Leibniz Institute for the Social Sciences Cologne Germany), Miriam Fernandez (0000-0001-5939-4321, The Open University), Harith Alani (0000-0003-2784-349X, The Open University), Bettina Berendt (0000-0002-8003-3413, Technische Universität Berlin), Tina Kruegel (Leibniz University Hannover), Christian Heinze (0000-0002-9114-8080, Leibniz University Hannover), Klaus Broelemann (Arbeitsgemeinschaft Gynäkologische Onkologie Studiengruppe), Gjergji Kasneci (0000-0002-3123-7268, Arbeitsgemeinschaft Gynäkologische Onkologie Studiengruppe), Thanassis Tiropanis (0000-0002-6195-2852, University of Southampton), Steffen Staab (0000-0002-0780-4154, University of Stuttgart)
Ano2020
Volume10
Fascículo3
Data de publicação2020-05-01
Peer ReviewedSim
Open AccessSim
TipoARTICLE
PeriódicoWIREs Data Mining and Knowledge Discovery (JOURNAL)
Identificadores do periódicoISSN: 1942-4787 • E-ISSN: 1942-4795
EditoraWiley (PUBLISHER • GB)
DOI10.1002/widm.1356
OpenAlexW3004493409
IdiomaEN
Citações recebidas92
Referências citadas63

Artificial Intelligence (AI)‐based systems are widely employed nowadays to make decisions that have far‐reaching impact on individuals and society. Their decisions might affect everyone, everywhere, and anytime, entailing concerns about potential human rights issues. Therefore, it is necessary to move beyond traditional AI algorithms optimized for predictive performance and embed ethical and legal principles in their design, training, and deployment to ensure social good while still benefiting from the huge potential of the AI technology. The goal of this survey is to provide a broad multidisciplinary overview of the area of bias in AI systems, focusing on technical challenges and solutions as well as to suggest new research directions towards approaches well‐grounded in a legal frame. In this survey, we focus on data‐driven AI, as a large part of AI is powered nowadays by (big) data and powerful machine learning algorithms. If otherwise not specified, we use the general term bias to describe problems related to the gathering or processing of data that might result in prejudiced decisions on the bases of demographic features such as race, sex, and so forth. This article is categorized under: Commercial, Legal, and Ethical Issues > Fairness in Data Mining Commercial, Legal, and Ethical Issues > Ethical Considerations Commercial, Legal, and Ethical Issues > Legal Issues

Big data · Data mining · Data science · Engineering ethics · Ethical issues · Multidisciplinary approach · Political science · Software deployment · Artificial Intelligence · Artificial Intelligence in Healthcare and Education · Computer Science · Ethics and Social Impacts of AI · Explainable Artificial Intelligence (XAI · Law