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Measurement Non-Invariance in Machin...

Suzuki, Honoka.

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Measurement Non-Invariance in Machine Learning: An Intersection of Machine Learning Bias and Test Bias.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Measurement Non-Invariance in Machine Learning: An Intersection of Machine Learning Bias and Test Bias./
Author:	Suzuki, Honoka.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
Description:	115 p.
Notes:	Source: Masters Abstracts International, Volume: 85-07.
Contained By:	Masters Abstracts International85-07.
Subject:	Quantitative psychology. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30689446
ISBN:	9798381384123

Measurement Non-Invariance in Machine Learning: An Intersection of Machine Learning Bias and Test Bias.
Suzuki, Honoka.

Measurement Non-Invariance in Machine Learning: An Intersection of Machine Learning Bias and Test Bias. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 115 p.

Source: Masters Abstracts International, Volume: 85-07.

Thesis (M.A.)--The University of North Carolina at Chapel Hill, 2023.

Algorithmic and machine learning bias have stirred concern in society as machine learning continues to channel into sensitive and high-stakes applications, including in healthcare, hiring, and criminal justice. While research surrounding machine learning bias may be relatively new, psychometricians have for decades researched a closely paralleled topic of test bias in psychological and educational testing. Leveraging the connection between these two fairness domains, this thesis studies the problem of machine learning bias from a measurement perspective, specifically focusing on measurement non-invariance in outcome variables as a source of machine learning bias. A framework is introduced, which conceptualizes machine learning bias in a psychometric sense and allows for tests of measurement invariance in machine learning. Using a Monte Carlo simulation study, the consequences of measurement bias on machine learning bias are demonstrated, as well as the effectiveness of a proposed bias mitigation technique to address these effects of measurement bias, which also follows from the proposed framework. The application of the proposed methods is illustrated with data from a large-scale health survey. Broader implications of the relevance of fairness in measurement for fairness in machine learning are discussed.

ISBN: 9798381384123Subjects--Topical Terms:

2144748
Quantitative psychology.
Subjects--Index Terms:

Algorithmic fairness

Measurement Non-Invariance in Machine Learning: An Intersection of Machine Learning Bias and Test Bias.
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