Predictive Algorithms and Perceptions of Fairness: Parent Attitudes Toward Algorithmic Resource Allocation in K-12 Education

As institutions increasingly use predictive algorithms to allocate scarce resources, scholars have warned that these algorithms may legitimize inequality. Although research has examined how elite discourses position algorithms as fair, we know less about how the public perceives them compared to tra...

Full description

Saved in:
Bibliographic Details
Main Authors: Rebecca A. Johnson, Simone Zhang
Format: Article
Language:English
Published: Society for Sociological Science 2025-05-01
Series:Sociological Science
Subjects:
Online Access:https://sociologicalscience.com/articles-v12-15-322/
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1850135070304632832
author Rebecca A. Johnson
Simone Zhang
author_facet Rebecca A. Johnson
Simone Zhang
author_sort Rebecca A. Johnson
collection DOAJ
description As institutions increasingly use predictive algorithms to allocate scarce resources, scholars have warned that these algorithms may legitimize inequality. Although research has examined how elite discourses position algorithms as fair, we know less about how the public perceives them compared to traditional allocation methods. We implement a vignette-based survey experiment to measure perceptions of algorithmic allocation relative to common alternatives: administrative rules, lotteries, petitions from potential beneficiaries, and professional judgment. Focusing on the case of schools allocating scarce tutoring resources, our nationally representative survey of U.S. parents finds that parents view algorithms as fairer than traditional alternatives, especially lotteries. However, significant divides emerge along socioeconomic and political lines—lower socioeconomic status (SES) and conservative parents favor the personal knowledge held by counselors and parents, whereas higher SES and liberal parents prefer the impersonal logic of algorithms. We also find that, after reading about algorithmic bias, parental opposition to algorithms is strongest among those who are most directly disadvantaged. Overall, our findings map cleavages in attitudes that may influence the adoption and political sustainability of algorithmic allocation methods.
format Article
id doaj-art-afe0aad0520744a3b36dbd46a9219cce
institution OA Journals
issn 2330-6696
language English
publishDate 2025-05-01
publisher Society for Sociological Science
record_format Article
series Sociological Science
spelling doaj-art-afe0aad0520744a3b36dbd46a9219cce2025-08-20T02:31:32ZengSociety for Sociological ScienceSociological Science2330-66962025-05-0112532235610.15195/v12.a15Predictive Algorithms and Perceptions of Fairness: Parent Attitudes Toward Algorithmic Resource Allocation in K-12 EducationRebecca A. Johnson0Simone Zhang1Georgetown UniversityNew York UniversityAs institutions increasingly use predictive algorithms to allocate scarce resources, scholars have warned that these algorithms may legitimize inequality. Although research has examined how elite discourses position algorithms as fair, we know less about how the public perceives them compared to traditional allocation methods. We implement a vignette-based survey experiment to measure perceptions of algorithmic allocation relative to common alternatives: administrative rules, lotteries, petitions from potential beneficiaries, and professional judgment. Focusing on the case of schools allocating scarce tutoring resources, our nationally representative survey of U.S. parents finds that parents view algorithms as fairer than traditional alternatives, especially lotteries. However, significant divides emerge along socioeconomic and political lines—lower socioeconomic status (SES) and conservative parents favor the personal knowledge held by counselors and parents, whereas higher SES and liberal parents prefer the impersonal logic of algorithms. We also find that, after reading about algorithmic bias, parental opposition to algorithms is strongest among those who are most directly disadvantaged. Overall, our findings map cleavages in attitudes that may influence the adoption and political sustainability of algorithmic allocation methods.https://sociologicalscience.com/articles-v12-15-322/predictive algorithmsalgorithmic decision-makingpublic perceptionsk-12 educationeducational inequalityresource allocation
spellingShingle Rebecca A. Johnson
Simone Zhang
Predictive Algorithms and Perceptions of Fairness: Parent Attitudes Toward Algorithmic Resource Allocation in K-12 Education
Sociological Science
predictive algorithms
algorithmic decision-making
public perceptions
k-12 education
educational inequality
resource allocation
title Predictive Algorithms and Perceptions of Fairness: Parent Attitudes Toward Algorithmic Resource Allocation in K-12 Education
title_full Predictive Algorithms and Perceptions of Fairness: Parent Attitudes Toward Algorithmic Resource Allocation in K-12 Education
title_fullStr Predictive Algorithms and Perceptions of Fairness: Parent Attitudes Toward Algorithmic Resource Allocation in K-12 Education
title_full_unstemmed Predictive Algorithms and Perceptions of Fairness: Parent Attitudes Toward Algorithmic Resource Allocation in K-12 Education
title_short Predictive Algorithms and Perceptions of Fairness: Parent Attitudes Toward Algorithmic Resource Allocation in K-12 Education
title_sort predictive algorithms and perceptions of fairness parent attitudes toward algorithmic resource allocation in k 12 education
topic predictive algorithms
algorithmic decision-making
public perceptions
k-12 education
educational inequality
resource allocation
url https://sociologicalscience.com/articles-v12-15-322/
work_keys_str_mv AT rebeccaajohnson predictivealgorithmsandperceptionsoffairnessparentattitudestowardalgorithmicresourceallocationink12education
AT simonezhang predictivealgorithmsandperceptionsoffairnessparentattitudestowardalgorithmicresourceallocationink12education