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...
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Society for Sociological Science
2025-05-01
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| Series: | Sociological Science |
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| Online Access: | https://sociologicalscience.com/articles-v12-15-322/ |
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| 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 |