Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Fairness Perception from a Network-Centric Perspective   Algorithmic fairness is a major concern in recent years as the influence of
machine learning algorithms becomes more widespread. In this paper, we
investigate the issue of algorithmic fairness from a network-centric
perspective. Specifically, we introduce a novel yet intuitive function known as
network-centric fairness perception and provide an axiomatic approach to
analyze its properties. Using a peer-review network as case study, we also
examine its utility in terms of assessing the perception of fairness in paper
acceptance decisions. We show how the function can be extended to a group
fairness metric known as fairness visibility and demonstrate its relationship
to demographic parity. We also illustrate a potential pitfall of the fairness
visibility measure that can be exploited to mislead individuals into perceiving
that the algorithmic decisions are fair. We demonstrate how the problem can be
alleviated by increasing the local neighborhood size of the fairness perception
function.

25
The categories of this paper are: patt-sol
6
The categories of this paper are: astro-ph
20
The categories of this paper are: acc-phys
37
The categories of this paper are: supr-con
31
The categories of this paper are: ao-sci
29
The categories of this paper are: funct-an
8
The categories of this paper are: hep-ex
18
The categories of this paper are: econ
28
The categories of this paper are: alg-geom
24
The categories of this paper are: dg-ga
10
The categories of this paper are: q-bio
30
The categories of this paper are: q-alg
32
The categories of this paper are: atom-ph
9
The categories of this paper are: nlin
12
The categories of this paper are: nucl-th
36
The categories of this paper are: cmp-lg