SAF: Stakeholders’ Agreement on Fairness in the Practice of Machine Learning Development

Georgina Curto, Flavio Comim

Producción científica: Artículo en revista indizadaArtículorevisión exhaustiva

Resumen

This paper clarifies why bias cannot be completely mitigated in Machine Learning (ML) and proposes an end-to-end methodology to translate the ethical principle of justice and fairness into the practice of ML development as an ongoing agreement with stakeholders. The pro-ethical iterative process presented in the paper aims to challenge asymmetric power dynamics in the fairness decision making within ML design and support ML development teams to identify, mitigate and monitor bias at each step of ML systems development. The process also provides guidance on how to explain the always imperfect trade-offs in terms of bias to users.

Idioma originalInglés
Número de artículo29
Número de páginas19
PublicaciónScience and Engineering Ethics
Volumen29
N.º4
DOI
EstadoPublicada - ago 2023

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