What Is Fairness? Philosophical Considerations and Implications For FairML

However, a fundamental question remains: What is fairness? This question is not so easy to answer and is often skipped; instead of asking "what is fairness", the questions of "how to measure fairness of ML models" and "how to make ML models fair" are pursued. This paper does not intend to criticize individual approaches that address those latter questions and often propose important solutions. Rather, the aim is to make explicit the premises that underlie the various understandings of fairness and the approaches to solving fairness problems. In doing so, a largely concordant understanding can be elaborated that is based on a rich foundation in the history of philosophy. Subsequently, we show that the conception of fairness depends on multilayered normative evaluations; any discussion of fairML is reliant on adopting those normative stipulations. The basis for fair decisions is always the question of the equality of the people treated with respect to the subject matter concerned. With this decision basis, a decision rule is to be established, which in turn can be adapted to the concrete needs as a result of normative stipulations. Based on this basic concept of fairness, we turn to the questions of to what extent ML models can induce unfair treatments in automated decision-making (ADM), and of how to implement these normative stipulations in training an ML model and in using its predictions in ADM.