Publication:

The man behind the curtain: appropriating fairness in AI

Date

Date

Date
2024
Journal Article
Published version

Citations

Citation copied

Korecki, M., Köstner, G., Martinelli, E., & Carissimo, C. (2024). The man behind the curtain: appropriating fairness in AI. Minds and Machines, 34, 7. https://doi.org/10.1007/s11023-024-09669-x

Abstract

Abstract

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 a

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45 since deposited on 2024-08-14
Acq. date: 2025-11-12

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57 since deposited on 2024-08-14
Acq. date: 2025-11-12

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Creators (Authors)

Journal/Series Title

Journal/Series Title

Journal/Series Title

Volume

Volume

Volume
34

Page range/Item number

Page range/Item number

Page range/Item number
7

Item Type

Item Type

Item Type
Journal Article

Dewey Decimal Classifikation

Dewey Decimal Classifikation

Dewey Decimal Classifikation

Keywords

AI fairness, AI normativity, Responsibility in AI

Language

Language

Language
English

Publication date

Publication date

Publication date
2024-04-25

Date available

Date available

Date available
2024-08-14

Publisher

Publisher

Publisher

ISSN or e-ISSN

ISSN or e-ISSN

ISSN or e-ISSN
0924-6495

OA Status

OA Status

OA Status
Hybrid

Free Access at

Free Access at

Free Access at
DOI

Metrics

Downloads

45 since deposited on 2024-08-14
Acq. date: 2025-11-12

Views

57 since deposited on 2024-08-14
Acq. date: 2025-11-12

Citations

Citations

Citation copied

Korecki, M., Köstner, G., Martinelli, E., & Carissimo, C. (2024). The man behind the curtain: appropriating fairness in AI. Minds and Machines, 34, 7. https://doi.org/10.1007/s11023-024-09669-x

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