Fair Enough? A map of the current limitations of the requirements to
have "fair" algorithms
- URL: http://arxiv.org/abs/2311.12435v2
- Date: Sun, 17 Dec 2023 08:45:26 GMT
- Title: Fair Enough? A map of the current limitations of the requirements to
have "fair" algorithms
- Authors: Alessandro Castelnovo, Nicole Inverardi, Gabriele Nanino, Ilaria
Giuseppina Penco, Daniele Regoli
- Abstract summary: Automated Decision-Making systems can perpetuate or even amplifying bias and unjust disparities.
It has prompted more and more layers of society, including policy makers, to call for "fair" algorithms.
There is a hiatus between what the society is demanding from Automated Decision-Making systems, and what this demand actually means in real-world scenarios.
- Score: 46.20942922922006
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: In the recent years, the raise in the usage and efficiency of Artificial
Intelligence and, more in general, of Automated Decision-Making systems has
brought with it an increasing and welcome awareness of the risks associated
with such systems. One of such risks is that of perpetuating or even amplifying
bias and unjust disparities present in the data from which many of these
systems learn to adjust and optimise their decisions. This awareness has on one
side encouraged several scientific communities to come up with more and more
appropriate ways and methods to assess, quantify, and possibly mitigate such
biases and disparities. On the other hand, it has prompted more and more layers
of society, including policy makers, to call for "fair" algorithms. We believe
that while a lot of excellent and multidisciplinary research is currently being
conducted, what is still fundamentally missing is the awareness that having
"fair" algorithms is per se a nearly meaningless requirement, that needs to be
complemented with a lot of additional societal choices to become actionable.
Namely, there is a hiatus between what the society is demanding from Automated
Decision-Making systems, and what this demand actually means in real-world
scenarios. In this work, we outline the key features of such a hiatus, and
pinpoint a list of fundamental ambiguities and attention points that we as a
society must address in order to give a concrete meaning to the increasing
demand of fairness in Automated Decision-Making systems.
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