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This paper clarifies why bias cannot be completely mitigated in MachineLearning (ML) and proposes an end-to-end methodology to translate the ethicalprinciple of justice and fairness into the practice of ML development as anongoing agreement with stakeholders. The pro-ethical iterative processpresented in the paper aims to challenge asymmetric power dynamics in thefairness decision making within ML design and support ML development teams toidentify, mitigate and monitor bias at each step of ML systems development. Theprocess also provides guidance on how to explain the always imperfecttrade-offs in terms of bias to users.

Fairness: from the ethical principle to the practice of Machine Learning development as an ongoing agreement with stakeholders