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

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dc.contributor.authorNtoutsi, Eirini
dc.contributor.authorFafalios, Pavlos
dc.contributor.authorGadiraju, Ujwal
dc.contributor.authorIosifidis, Vasileios
dc.contributor.authorNejdl, Wolfgang
dc.contributor.authorVidal, Maria‐Esther
dc.contributor.authorRuggieri, Salvatore
dc.contributor.authorTurini, Franco
dc.contributor.authorPapadopoulos, Symeon
dc.contributor.authorKrasanakis, Emmanouil
dc.contributor.authorKompatsiaris, Ioannis
dc.contributor.authorKinder‐Kurlanda, Katharina
dc.contributor.authorWagner, Claudia
dc.contributor.authorKarimi, Fariba
dc.contributor.authorFernandez, Miriam
dc.contributor.authorAlani, Harith
dc.contributor.authorBerendt, Bettina
dc.contributor.authorKruegel, Tina
dc.contributor.authorHeinze, Christian
dc.contributor.authorBroelemann, Klaus
dc.contributor.authorKasneci, Gjergji
dc.contributor.authorTiropanis, Thanassis
dc.contributor.authorStaab, Steffen
dc.date.accessioned2024-05-02T15:00:13Z
dc.date.available2024-05-02T15:00:13Z
dc.date.issued2020
dc.description.abstractArtificial 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.
dc.description.sponsorshipEuropean Commission, Grant/Award Number: 860630
dc.identifier.citationNtoutsi, E., Fafalios, P., Gadiraju, U., e.a. (2020). Bias in data‐driven artificial intelligence systems—An introductory survey. WIREs Data Mining and Knowledge Discovery, 10(3), e1356. https://doi.org/10.1002/widm.1356
dc.identifier.doihttps://doi.org/10.1002/widm.1356
dc.identifier.issn1942-4787
dc.identifier.issn1942-4795
dc.identifier.urihttps://www.weizenbaum-library.de/handle/id/600
dc.language.isoeng
dc.rightsopen access
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleBias in data‐driven artificial intelligence systems—An introductory survey
dc.typeArticle
dc.type.statuspublishedVersion
dcmi.typeText
dcterms.bibliographicCitation.urlhttps://doi.org/10.1002/widm.1356
local.researchgroupKritikalität KI-basierter Systeme
local.researchgroupVerantwortung und das Internet der Dinge
local.researchtopicVerantwortung – Vertrauen – Governance
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