Legal perspective on possible fairness measures - A legal discussion using the example of hiring decisions (preprint)

TL;DR

This paper examines the legal implications of different fairness measures in AI-driven hiring decisions, emphasizing the importance of selecting appropriate fairness concepts aligned with legal standards and societal values.

Contribution

It provides a legal analysis of various fairness measures in AI, highlighting the need for scenario-specific fairness definitions in employment contexts.

Findings

Different fairness concepts have distinct legal and societal implications.

A shift from process to result-oriented fairness affects legal assessments.

Legal evaluation of fairness measures depends on context and societal norms.

Abstract

With the increasing use of AI in algorithmic decision making (e.g. based on neural networks), the question arises how bias can be excluded or mitigated. There are some promising approaches, but many of them are based on a "fair" ground truth, others are based on a subjective goal to be reached, which leads to the usual problem of how to define and compute "fairness". The different functioning of algorithmic decision making in contrast to human decision making leads to a shift from a process-oriented to a result-oriented discrimination assessment. We argue that with such a shift society needs to determine which kind of fairness is the right one to choose for which certain scenario. To understand the implications of such a determination we explain the different kinds of fairness concepts that might be applicable for the specific application of hiring decisions, analyze their pros and cons with regard to the respective fairness interpretation and evaluate them from a legal perspective (based on EU law).