Details
| Original language | English |
|---|---|
| Article number | e1356 |
| Journal | Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery |
| Volume | 10 |
| Issue number | 3 |
| Early online date | 3 Feb 2020 |
| Publication status | Published - 16 Apr 2020 |
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.
Keywords
- fairness, fairness-aware AI, fairness-aware machine learning, interpretability, responsible AI
ASJC Scopus subject areas
- Computer Science(all)
- General Computer Science
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In: Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, Vol. 10, No. 3, e1356, 16.04.2020.
Research output: Contribution to journal › Review article › Research › peer review
}
TY - JOUR
T1 - Bias in data-driven artificial intelligence systems
T2 - An introductory survey
AU - Ntoutsi, Eirini
AU - Fafalios, Pavlos
AU - Gadiraju, Ujwal
AU - Iosifidis, Vasileios
AU - Nejdl, Wolfgang
AU - Vidal, Maria Esther
AU - Ruggieri, Salvatore
AU - Turini, Franco
AU - Papadopoulos, Symeon
AU - Krasanakis, Emmanouil
AU - Kompatsiaris, Ioannis
AU - Kinder-Kurlanda, Katharina
AU - Wagner, Claudia
AU - Karimi, Fariba
AU - Fernandez, Miriam
AU - Alani, Harith
AU - Berendt, Bettina
AU - Kruegel, Tina
AU - Heinze, Christian
AU - Broelemann, Klaus
AU - Kasneci, Gjergji
AU - Tiropanis, Thanassis
AU - Staab, Steffen
N1 - 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 Skłodowska‐Curie (Innovative Training Network) grant agreement no. 860630.
PY - 2020/4/16
Y1 - 2020/4/16
N2 - 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.
AB - 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.
KW - fairness
KW - fairness-aware AI
KW - fairness-aware machine learning
KW - interpretability
KW - responsible AI
UR - http://www.scopus.com/inward/record.url?scp=85078894838&partnerID=8YFLogxK
U2 - 10.1002/widm.1356
DO - 10.1002/widm.1356
M3 - Review article
AN - SCOPUS:85078894838
VL - 10
JO - Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
JF - Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
SN - 1942-4787
IS - 3
M1 - e1356
ER -