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

arXiv: https://arxiv.org/abs/2108.06918 · HTML: https://mlsanity.com/paper/2108.06918/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Aug 2021.

## Built on by (most cited first, 4 of 4)

- [Multi-dimensional discrimination in Law and Machine Learning -- A comparative overview](https://mlsanity.com/paper/2302.05995/) (2023)
- [Implications of the AI Act for Non-Discrimination Law and Algorithmic Fairness](https://mlsanity.com/paper/2403.20089/) (2024)
- [(Machine) Learning to Be Like Thee? For Algorithm Education, Not Training](https://mlsanity.com/paper/2305.12157/) (2023)
- [FALE: Fairness-Aware ALE Plots for Auditing Bias in Subgroups](https://mlsanity.com/paper/2404.18685/) (2024)

## Abstract (arXiv)

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).