2026-04-13T00:58:29Zhttps://recercat.cat/oai/request

oai:recercat.cat:20.500.14342/46922025-05-23T06:08:57Zcom_2072_482405com_2072_183628col_2072_482414

00925njm 22002777a 4500 dc Curto, Georgina author Comim, Flavio author 2023 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. 1471-5546 http://hdl.handle.net/20.500.14342/4692 https://doi.org/10.1007/s11948-023-00448-y Bias Artificial Intelligence Trustworthy AI Fairness Discrimination Pro-Ethical Design Intel·ligència artificial Confiança Imparcialitat Discriminació Ètica SAF: Stakeholders’ Agreement on Fairness in the Practice of Machine Learning Development