  The potential risk of AI systems unintentionally embedding and reproducing
bias has attracted the attention of machine learning practitioners and society
at large. As policy makers are willing to set the standards of algorithms and
AI techniques, the issue on how to refine existing regulation, in order to
enforce that decisions made by automated systems are fair and
non-discriminatory, is again critical. Meanwhile, researchers have demonstrated
that the various existing metrics for fairness are statistically mutually
exclusive and the right choice mostly depends on the use case and the
definition of fairness.
  Recognizing that the solutions for implementing fair AI are not purely
mathematical but require the commitments of the stakeholders to define the
desired nature of fairness, this paper proposes to draft a toolbox which helps
practitioners to ensure fair AI practices. Based on the nature of the
application and the available training data, but also on legal requirements and
ethical, philosophical and cultural dimensions, the toolbox aims to identify
the most appropriate fairness objective. This approach attempts to structure
the complex landscape of fairness metrics and, therefore, makes the different
available options more accessible to non-technical people. In the proven
absence of a silver bullet solution for fair AI, this toolbox intends to
produce the fairest AI systems possible with respect to their local context.
