arXiv Machine Learning June 4, 2026 By Yuri Faenza, Swati Gupta, Aapeli Vuorinen, Xuan Zhang

Reducing the Filtering Effect in Public School Admissions: A Bias-aware Analysis for Targeted Interventions

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arXiv:2004. 10846v5 Announce Type: replace-cross Abstract: Problem definition: Traditionally, New York City's top 8 public schools have selected candidates solely based on their scores in the Specialized High School Admissions Test (SHSAT).

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arXiv Machine Learning

Sep 22

Actionable Insights from Observational Data: The Case of Advanced Classes in K-12 Education

arXiv:2609.23836v1 Announce Type: new Abstract: A fundamentally challenging question in K-12 education is about the effects of taking more advanced or challenging classes. It is particularly complex...

By Nabit Bajwa, Seth B. Hunter, Sanmay Das

arXiv Machine Learning

Sep 22

Identifying Representational Biases in Datasets Using PCA: A Max-Disparity Partition Framework

arXiv:2609.24556v1 Announce Type: cross Abstract: Principal Component Analysis (PCA) minimises aggregate reconstruction error, which can inadvertently represent majority subgroups with substantially...

By Arjun KM, Shashi Jain

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arXiv Machine Learning

Jun 19

Data Bias Mitigation under Coverage Constraints & The Price of Fairness

arXiv:2606. 20461v1 Announce Type: new Abstract: Machine learning models have been shown to exhibit discriminatory outcomes or degraded performance for individuals at the intersection of multiple sensitive attributes, such as race and gender.

By Bruno Scarone, Alfredo Viola, Ren\'ee J. Miller

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arXiv Machine Learning

Jun 9

Generalizing Fair Top-$k$ Selection: An Integrative Approach

arXiv:2603. 04689v3 Announce Type: replace-cross Abstract: Fair top-$k$ selection, which ensures appropriate proportional representation of members from minority or historically disadvantaged groups among the top-$k$ selected candidates, has drawn significant attention.

By Guangya Cai

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arXiv Machine Learning

2d ago

Fairness Theatre: Evaluating Post-Hoc Fairness Interventions in Vendor-Controlled Early Warning Systems

arXiv:2609.38552v1 Announce Type: cross Abstract: Public institutions increasingly procure AI systems whose design they cannot inspect or change. In higher education, proprietary Early Warning System...

By Kelly McConvey, Angelina Zhai, Rebecca Li, Shion Guha

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arXiv Machine Learning

Sep 18

When fairness metrics fail: A utility-based perspective on $\varepsilon$-fairness

The paper argues that traditional probabilistic fairness metrics can miss significant disparities in the actual consequences of decisions. By introducing a utility-based framework, the authors show that a process can satisfy ε-fairness yet still be maximally unfair when utilities are considered. They apply this framework to college admissions and credit‑risk assessment, demonstrating that equalizing probabilities alone may mask unequal utility outcomes across groups.

By Tolulope Fadina, Thorsten Schmidt

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Actionable Insights from Observational Data: The Case of Advanced Classes in K-12 Education

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Data Bias Mitigation under Coverage Constraints & The Price of Fairness

Generalizing Fair Top-$k$ Selection: An Integrative Approach

Fairness Theatre: Evaluating Post-Hoc Fairness Interventions in Vendor-Controlled Early Warning Systems

When fairness metrics fail: A utility-based perspective on $\varepsilon$-fairness