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

Bias in data‐driven artificial intelligence systems: An introductory survey
Bias in data‐driven artificial intelligence systems: An introductory survey
AI-based systems are widely employed nowadays to make decisions that have far-reaching impacts 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 multi-disciplinary 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 (ML) 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 like race, sex, etc.
1942-4795
1-14
Ntoutsi, Eirini
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Fafalios, Pavlos
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Gadiraju, Ujwal
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Iosifidis, Vasileios
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Nejdl, Wolfgang
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Vidal, Maria-Esther
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Ruggieri, Salvatore
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Turini, Franco
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Papadopoulos, Symeon
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Krasanakis, Emmanouil
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Kompatsiaris, Ioannis
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Kinder-Kurlanda, Katharina
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Wagner, Claudia
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Karimi, Fariba
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Fernández, Miriam
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Alani, Harith
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Berendt, Bettina
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Krügel, Tina
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Heinze, Christian
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Broelemann, Klaus
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Kasneci, Gjergji
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Tiropanis, Thanassis
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Staab, Steffen
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Ntoutsi, Eirini
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Fafalios, Pavlos
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Gadiraju, Ujwal
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Iosifidis, Vasileios
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Nejdl, Wolfgang
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Vidal, Maria-Esther
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Ruggieri, Salvatore
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Turini, Franco
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Papadopoulos, Symeon
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Krasanakis, Emmanouil
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Kompatsiaris, Ioannis
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Kinder-Kurlanda, Katharina
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Wagner, Claudia
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Karimi, Fariba
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Fernández, Miriam
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Alani, Harith
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Berendt, Bettina
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Krügel, Tina
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Heinze, Christian
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Broelemann, Klaus
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Kasneci, Gjergji
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Tiropanis, Thanassis
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Staab, Steffen
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Ntoutsi, Eirini, Fafalios, Pavlos, Gadiraju, Ujwal, Iosifidis, Vasileios, Nejdl, Wolfgang, Vidal, Maria-Esther, Ruggieri, Salvatore, Turini, Franco, Papadopoulos, Symeon, Krasanakis, Emmanouil, Kompatsiaris, Ioannis, Kinder-Kurlanda, Katharina, Wagner, Claudia, Karimi, Fariba, Fernández, Miriam, Alani, Harith, Berendt, Bettina, Krügel, Tina, Heinze, Christian, Broelemann, Klaus, Kasneci, Gjergji, Tiropanis, Thanassis and Staab, Steffen (2020) Bias in data‐driven artificial intelligence systems: An introductory survey. WIREs Data Mining and Knowledge Discovery, 1-14, [e1356]. (doi:10.1002/widm.1356).

Record type: Article

Abstract

AI-based systems are widely employed nowadays to make decisions that have far-reaching impacts 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 multi-disciplinary 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 (ML) 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 like race, sex, etc.

Text
Ntoutsi et al 2020 Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery - Version of Record
Available under License Creative Commons Attribution.
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Accepted/In Press date: 31 December 2019
e-pub ahead of print date: 3 February 2020

Identifiers

Local EPrints ID: 437566
URI: http://eprints.soton.ac.uk/id/eprint/437566
ISSN: 1942-4795
PURE UUID: 7ad486fc-27cb-43df-b0de-9716abd7d7b2
ORCID for Thanassis Tiropanis: ORCID iD orcid.org/0000-0002-6195-2852
ORCID for Steffen Staab: ORCID iD orcid.org/0000-0002-0780-4154

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Date deposited: 05 Feb 2020 17:34
Last modified: 13 Mar 2020 01:35

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Contributors

Author: Eirini Ntoutsi
Author: Pavlos Fafalios
Author: Ujwal Gadiraju
Author: Vasileios Iosifidis
Author: Wolfgang Nejdl
Author: Maria-Esther Vidal
Author: Salvatore Ruggieri
Author: Franco Turini
Author: Symeon Papadopoulos
Author: Emmanouil Krasanakis
Author: Ioannis Kompatsiaris
Author: Katharina Kinder-Kurlanda
Author: Claudia Wagner
Author: Fariba Karimi
Author: Miriam Fernández
Author: Harith Alani
Author: Bettina Berendt
Author: Tina Krügel
Author: Christian Heinze
Author: Klaus Broelemann
Author: Gjergji Kasneci
Author: Thanassis Tiropanis ORCID iD
Author: Steffen Staab ORCID iD

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