Bias in data-driven artificial intelligence systems

An introductory survey

verfasst von

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

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.

Organisationseinheit(en)

Forschungszentrum L3S
Institut für Rechtsinformatik (IRI)

Externe Organisation(en)

University of Pisa
Center For Research And Technology - Hellas
GESIS - Leibniz-Institut für Sozialwissenschaften
The Open University
Technische Universität Berlin
KU Leuven
SCHUFA Holding AG
University of Southampton
Universität Stuttgart
Foundation for Research & Technology - Hellas (FORTH)
Technische Informationsbibliothek (TIB) Leibniz-Informationszentrum Technik und Naturwissenschaften und Universitätsbibliothek

Typ

Übersichtsarbeit

Journal

Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery

Band

10

ISSN

1942-4787

Publikationsdatum

16.04.2020

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Allgemeine Computerwissenschaft

Elektronische Version(en)

https://doi.org/10.1002/widm.1356 (Zugang: Offen)
https://doi.org/10.48550/arXiv.2001.09762 (Zugang: Offen)