Fairness: from the ethical principle to the practice of Machine Learning development as an ongoing agreement with stakeholders. (arXiv:2304.06031v1 [cs.CY])

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.