Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Legal perspective on possible fairness measures - A legal discussion
  using the example of hiring decisions (preprint)   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).

6
The categories of this paper are: astro-ph
23
The categories of this paper are: solv-int
20
The categories of this paper are: acc-phys
21
The categories of this paper are: adap-org
24
The categories of this paper are: dg-ga
11
The categories of this paper are: hep-lat
2
The categories of this paper are: math
28
The categories of this paper are: alg-geom
36
The categories of this paper are: cmp-lg
18
The categories of this paper are: econ
34
The categories of this paper are: chem-ph
3
The categories of this paper are: physics
16
The categories of this paper are: stat
14
The categories of this paper are: math-ph
7
The categories of this paper are: hep-th
31
The categories of this paper are: ao-sci