The fairness in algorithmic fairness

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The fairness in algorithmic fairness. / Holm, Sune.

In: Res Publica, Vol. 29, 2023, p. 265–281.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Holm, S 2023, 'The fairness in algorithmic fairness', Res Publica, vol. 29, pp. 265–281. https://doi.org/10.1007/s11158-022-09546-3

APA

Holm, S. (2023). The fairness in algorithmic fairness. Res Publica, 29, 265–281. https://doi.org/10.1007/s11158-022-09546-3

Vancouver

Holm S. The fairness in algorithmic fairness. Res Publica. 2023;29:265–281. https://doi.org/10.1007/s11158-022-09546-3

Author

Holm, Sune. / The fairness in algorithmic fairness. In: Res Publica. 2023 ; Vol. 29. pp. 265–281.

Bibtex

@article{977e2817213a4361b880cd375276fee8,
title = "The fairness in algorithmic fairness",
abstract = "With the increasing use of algorithms in high-stakes areas such as criminal justice and health has come a significant concern about the fairness of prediction-based decision procedures. In this article I argue that a prominent class of mathematically incompatible performance parity criteria can all be understood as applications of John Broome{\textquoteright}s account of fairness as the proportional satisfaction of claims. On this interpretation these criteria do not disagree on what it means for an algorithm to be fair. Rather they express different understandings of what grounds a claim to a good being allocated by an algorithmic decision procedure. I then argue that an important implication of the Broomean interpretation is that it strengthens the case for outcome-based criteria. Finally, I consider how a version of the levelling-down objection to performance parity criteria arises within the Broomean account.",
author = "Sune Holm",
year = "2023",
doi = "10.1007/s11158-022-09546-3",
language = "English",
volume = "29",
pages = "265–281",
journal = "Res Publica",
issn = "1356-4765",
publisher = "Springer",

}

RIS

TY - JOUR

T1 - The fairness in algorithmic fairness

AU - Holm, Sune

PY - 2023

Y1 - 2023

N2 - With the increasing use of algorithms in high-stakes areas such as criminal justice and health has come a significant concern about the fairness of prediction-based decision procedures. In this article I argue that a prominent class of mathematically incompatible performance parity criteria can all be understood as applications of John Broome’s account of fairness as the proportional satisfaction of claims. On this interpretation these criteria do not disagree on what it means for an algorithm to be fair. Rather they express different understandings of what grounds a claim to a good being allocated by an algorithmic decision procedure. I then argue that an important implication of the Broomean interpretation is that it strengthens the case for outcome-based criteria. Finally, I consider how a version of the levelling-down objection to performance parity criteria arises within the Broomean account.

AB - With the increasing use of algorithms in high-stakes areas such as criminal justice and health has come a significant concern about the fairness of prediction-based decision procedures. In this article I argue that a prominent class of mathematically incompatible performance parity criteria can all be understood as applications of John Broome’s account of fairness as the proportional satisfaction of claims. On this interpretation these criteria do not disagree on what it means for an algorithm to be fair. Rather they express different understandings of what grounds a claim to a good being allocated by an algorithmic decision procedure. I then argue that an important implication of the Broomean interpretation is that it strengthens the case for outcome-based criteria. Finally, I consider how a version of the levelling-down objection to performance parity criteria arises within the Broomean account.

U2 - 10.1007/s11158-022-09546-3

DO - 10.1007/s11158-022-09546-3

M3 - Journal article

VL - 29

SP - 265

EP - 281

JO - Res Publica

JF - Res Publica

SN - 1356-4765

ER -

ID: 298120488