2026-04-18T00:30:57Zhttps://recercat.cat/oai/requestoai:recercat.cat:20.500.14342/46922025-05-23T06:08:57Zcom_2072_482405com_2072_183628col_2072_482414
RECERCAT
author
Curto, Georgina
author
Comim, Flavio
2023
1471-5546
http://hdl.handle.net/20.500.14342/4692
https://doi.org/10.1007/s11948-023-00448-y
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.
eng
© L'autor/a Attribution 4.0 International
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
info:eu-repo/semantics/article