SAF: Stakeholders’ Agreement on Fairness in the Practice of Machine Learning Development
Appeared In: Science and Engineering Ethics
Publication Date: July 24, 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.
Curto, G., Comim, F. SAF: Stakeholders’ Agreement on Fairness in the Practice of Machine Learning Development. Sci Eng Ethics 29, 29 (2023). DOI: 10.1007/s11948-023-00448-y