Bias in data‐driven artificial intelligence systems: An introductory survey
Bias in data‐driven artificial intelligence systems: An introductory survey
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.
fairness, fairness-aware AI, fairness-aware machine learning, interpretability, responsible AI
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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1 May 2020
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, 10 (3), , [e1356].
(doi:10.1002/widm.1356).
Abstract
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.
Text
Ntoutsi et al 2020 Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery
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More information
Accepted/In Press date: 31 December 2019
e-pub ahead of print date: 3 February 2020
Published date: 1 May 2020
Additional Information:
Funding Information:
This work is supported by the project ?NoBias - Artificial Intelligence without Bias,? which has received funding from the European Union's Horizon 2020 research and innovation programme, under the Marie Sk?odowska-Curie (Innovative Training Network) grant agreement no. 860630.
Publisher Copyright:
© 2020 The Authors. WIREs Data Mining and Knowledge Discovery published by Wiley Periodicals, Inc.
Keywords:
fairness, fairness-aware AI, fairness-aware machine learning, interpretability, responsible AI
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Local EPrints ID: 437566
URI: http://eprints.soton.ac.uk/id/eprint/437566
ISSN: 1942-4795
PURE UUID: 7ad486fc-27cb-43df-b0de-9716abd7d7b2
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Date deposited: 05 Feb 2020 17:34
Last modified: 06 Jun 2024 01:54
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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
Author:
Steffen Staab
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