Open access
Date
2024-04-25Type
- Journal Article
ETH Bibliography
yes
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Abstract
Our goal in this paper is to establish a set of criteria for understanding the meaning and sources of attributing (un)fairness to AI algorithms. To do so, we first establish that (un)fairness, like other normative notions, can be understood in a proper primary sense and in secondary senses derived by analogy. We argue that AI algorithms cannot be said to be (un)fair in the proper sense due to a set of criteria related to normativity and agency. However, we demonstrate how and why AI algorithms can be qualified as (un)fair by analogy and explore the sources of this (un)fairness and the associated problems of responsibility assignment. We conclude that more user-driven AI approaches could alleviate some of these difficulties. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000671294Publication status
publishedExternal links
Journal / series
Minds and MachinesVolume
Pages / Article No.
Publisher
SpringerSubject
AI fariness; AI normativity; Responsibility in AIOrganisational unit
02150 - Dep. Informatik / Dep. of Computer Science
Funding
833168 - Co-Evolving City Life (EC)
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ETH Bibliography
yes
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