  To implement fair machine learning in a sustainable way, choosing the right
fairness objective is key. Since fairness is a concept of justice which comes
in various, sometimes conflicting definitions, this is not a trivial task
though. The most appropriate fairness definition for an artificial intelligence
(AI) system is a matter of ethical standards and legal requirements, and the
right choice depends on the particular use case and its context. In this
position paper, we propose to use a decision tree as means to explain and
justify the implemented kind of fairness to the end users. Such a structure
would first of all support AI practitioners in mapping ethical principles to
fairness definitions for a concrete application and therefore make the
selection a straightforward and transparent process. However, this approach
would also help document the reasoning behind the decision making. Due to the
general complexity of the topic of fairness in AI, we argue that specifying
"fairness" for a given use case is the best way forward to maintain confidence
in AI systems. In this case, this could be achieved by sharing the reasons and
principles expressed during the decision making process with the broader
audience.
