Policy advice and best practices on bias and fairness in AI

Alvarez, Jose M.; Colmenarejo, Alejandra Bringas; Elobaid, Alaa; Fabbrizzi, Simone; Fahimi, Miriam; Ferrara, Antonio; Ghodsi, Siamak; Mougan, Carlos; Papageorgiou, Ioanna; Reyero, Paula; Russo, Mayra; Scott, Kristen M.; State, Laura; Zhao, Xuan and Ruggieri, Salvatore (2024). Policy advice and best practices on bias and fairness in AI. Ethics and Information Technology, 26, article no. 31.

DOI: https://doi.org/10.1007/s10676-024-09746-w


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.

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