Details
| Originalsprache | Englisch |
|---|---|
| Aufsatznummer | 31 |
| Seitenumfang | 26 |
| Fachzeitschrift | Ethics and information technology |
| Jahrgang | 26 |
| Ausgabenummer | 2 |
| Frühes Online-Datum | 29 Apr. 2024 |
| Publikationsstatus | Veröffentlicht - Juni 2024 |
Abstract
The literature addressing bias and fairness in AI models (fair-AI) is growing at a fast pace, making it difficult for novel researchers and practitioners to have a bird’s-eye view picture of the field. In particular, many policy initiatives, standards, and best practices in fair-AI have been proposed for setting principles, procedures, and knowledge bases to guide and operationalize the management of bias and fairness. The first objective of this paper is to concisely survey the state-of-the-art of fair-AI methods and resources, and the main policies on bias in AI, with the aim of providing such a bird’s-eye guidance for both researchers and practitioners. The second objective of the paper is to contribute to the policy advice and best practices state-of-the-art by leveraging from the results of the NoBIAS research project. We present and discuss a few relevant topics organized around the NoBIAS architecture, which is made up of a Legal Layer, focusing on the European Union context, and a Bias Management Layer, focusing on understanding, mitigating, and accounting for bias.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Angewandte Informatik
- Sozialwissenschaften (insg.)
- Bibliotheks- und Informationswissenschaften
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in: Ethics and information technology, Jahrgang 26, Nr. 2, 31, 06.2024.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Policy advice and best practices on bias and fairness in AI
AU - Alvarez, Jose M.
AU - Colmenarejo, Alejandra Bringas
AU - Elobaid, Alaa
AU - Fabbrizzi, Simone
AU - Fahimi, Miriam
AU - Ferrara, Antonio
AU - Ghodsi, Siamak
AU - Mougan, Carlos
AU - Papageorgiou, Ioanna
AU - Reyero, Paula
AU - Russo, Mayra
AU - Scott, Kristen M.
AU - State, Laura
AU - Zhao, Xuan
AU - Ruggieri, Salvatore
N1 - Publisher Copyright: © The Author(s) 2024.
PY - 2024/6
Y1 - 2024/6
N2 - The literature addressing bias and fairness in AI models (fair-AI) is growing at a fast pace, making it difficult for novel researchers and practitioners to have a bird’s-eye view picture of the field. In particular, many policy initiatives, standards, and best practices in fair-AI have been proposed for setting principles, procedures, and knowledge bases to guide and operationalize the management of bias and fairness. The first objective of this paper is to concisely survey the state-of-the-art of fair-AI methods and resources, and the main policies on bias in AI, with the aim of providing such a bird’s-eye guidance for both researchers and practitioners. The second objective of the paper is to contribute to the policy advice and best practices state-of-the-art by leveraging from the results of the NoBIAS research project. We present and discuss a few relevant topics organized around the NoBIAS architecture, which is made up of a Legal Layer, focusing on the European Union context, and a Bias Management Layer, focusing on understanding, mitigating, and accounting for bias.
AB - The literature addressing bias and fairness in AI models (fair-AI) is growing at a fast pace, making it difficult for novel researchers and practitioners to have a bird’s-eye view picture of the field. In particular, many policy initiatives, standards, and best practices in fair-AI have been proposed for setting principles, procedures, and knowledge bases to guide and operationalize the management of bias and fairness. The first objective of this paper is to concisely survey the state-of-the-art of fair-AI methods and resources, and the main policies on bias in AI, with the aim of providing such a bird’s-eye guidance for both researchers and practitioners. The second objective of the paper is to contribute to the policy advice and best practices state-of-the-art by leveraging from the results of the NoBIAS research project. We present and discuss a few relevant topics organized around the NoBIAS architecture, which is made up of a Legal Layer, focusing on the European Union context, and a Bias Management Layer, focusing on understanding, mitigating, and accounting for bias.
KW - Artificial Intelligence
KW - Best practices
KW - Bias
KW - Fairness
KW - Policy advice
UR - http://www.scopus.com/inward/record.url?scp=85196125498&partnerID=8YFLogxK
U2 - 10.1007/s10676-024-09746-w
DO - 10.1007/s10676-024-09746-w
M3 - Article
AN - SCOPUS:85196125498
VL - 26
JO - Ethics and information technology
JF - Ethics and information technology
SN - 1388-1957
IS - 2
M1 - 31
ER -