01938nas a2200241   4500008004100000245008100041210007100122300001000193490000700203520118800210100002001398700002101418700002001439700002501459700002001484700002401504700002401528700001901552700002501571700002601596700001101622856006301633       2020                        eng d00aBias in data-driven artificial intelligence systems—An introductory survey0 aBias in datadriven artificial intelligence systems—An introducto  ae13560 v103 aArtificial 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.1 aNtoutsi, Eirini1 aFafalios, Pavlos1 aGadiraju, Ujwal1 aIosifidis, Vasileios1 aNejdl, Wolfgang1 aVidal, Maria-Esther1 aRuggieri, Salvatore1 aTurini, Franco1 aPapadopoulos, Symeon1 aKrasanakis, Emmanouil1 aothers  uhttps://onlinelibrary.wiley.com/doi/full/10.1002/widm.1356