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Systemic Fairness and College Admissions.

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Τίτλος: Systemic Fairness and College Admissions.
Συγγραφείς: Ray, Arindam1 (AUTHOR) arindamray@uncg.edu, Padmanabhan, Balaji2 (AUTHOR) bpadmana@umd.edu, Bouayad, Lina3 (AUTHOR) dr.lina@applina.ai
Πηγή: INFORMS Journal on Computing. Jul/Aug2026, Vol. 38 Issue 4, p1198-1212. 15p.
Θεματικοί όροι: *Organizational justice, *Economic exclusion, *Artificial intelligence, University & college admission, Fairness, Policy analysis, Multiagent systems
Περίληψη: The increasing volume and complexity of applications to some institutions may make artificial intelligence (AI) a useful tool in college admission process. However, the integration of AI also raises concerns about fairness, particularly across institutions with varying selectivity. Using agent-based simulations grounded in real-world data, this study examines how AI sophistication, institutional selection criteria, opportunity constraints (e.g., fewer universities), and application constraints driven by socioeconomic barriers affect systemic disparities. We extend existing fairness metrics of demographic parity and equal opportunity to the system level and introduce a new metric, choice parity. Results show that selection criteria homogeneity and differences in AI sophistication can amplify inequities, especially when underrepresented students face constrained application options. Although primarily simulation based, this study highlights issues and considerations for policy makers regarding how institutional factors and AI use in the admissions process can potentially impact fairness at a systemic level and offers some policy guidance in this context. History: This paper has been accepted by Kaushik Dutta for the Special Issue on Responsible AI and Data Science for Social Good. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0523) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2023.0523). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]
Copyright of INFORMS Journal on Computing is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: The increasing volume and complexity of applications to some institutions may make artificial intelligence (AI) a useful tool in college admission process. However, the integration of AI also raises concerns about fairness, particularly across institutions with varying selectivity. Using agent-based simulations grounded in real-world data, this study examines how AI sophistication, institutional selection criteria, opportunity constraints (e.g., fewer universities), and application constraints driven by socioeconomic barriers affect systemic disparities. We extend existing fairness metrics of demographic parity and equal opportunity to the system level and introduce a new metric, choice parity. Results show that selection criteria homogeneity and differences in AI sophistication can amplify inequities, especially when underrepresented students face constrained application options. Although primarily simulation based, this study highlights issues and considerations for policy makers regarding how institutional factors and AI use in the admissions process can potentially impact fairness at a systemic level and offers some policy guidance in this context. History: This paper has been accepted by Kaushik Dutta for the Special Issue on Responsible AI and Data Science for Social Good. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0523) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2023.0523). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of INFORMS Journal on Computing is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – SubjectFull: Economic exclusion
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      – SubjectFull: Artificial intelligence
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      – SubjectFull: University & college admission
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      – SubjectFull: Policy analysis
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      – SubjectFull: Multiagent systems
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              Text: Jul/Aug2026
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