# A Human-Centered Review of Algorithms in Decision-Making in Higher Education

arXiv: https://arxiv.org/abs/2302.05839 · HTML: https://mlsanity.com/paper/2302.05839/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 131 references that are in the map.
- First posted on arXiv 12 Feb 2023.

## Builds on (most cited first, 8 of 8)

- [Counterfactual Fairness](https://mlsanity.com/paper/1703.06856/) (2017)
- [Roles for Computing in Social Change](https://mlsanity.com/paper/1912.04883/) (2019)
- [A Human-Centered Review of the Algorithms used within the U.S. Child Welfare System](https://mlsanity.com/paper/2003.03541/) (2020)
- [Towards Equity and Algorithmic Fairness in Student Grade Prediction](https://mlsanity.com/paper/2105.06604/) (2021)
- [Combating the Filter Bubble: Designing for Serendipity in a University Course Recommendation System](https://mlsanity.com/paper/1907.01591/) (2019)
- [Should College Dropout Prediction Models Include Protected Attributes?](https://mlsanity.com/paper/2103.15237/) (2021)
- [Evaluation of Fairness Trade-offs in Predicting Student Success](https://mlsanity.com/paper/2007.00088/) (2020)
- [An Uncommon Task: Participatory Design in Legal AI](https://mlsanity.com/paper/2203.06246/) (2022)

## Built on by (most cited first, 4 of 4)

- ["This is not a data problem": Algorithms and Power in Public Higher Education in Canada](https://mlsanity.com/paper/2403.13969/) (2024)
- [Beyond Predictive Algorithms in Child Welfare](https://mlsanity.com/paper/2403.05573/) (2024)
- [Fairness Audits of Institutional Risk Models in Deployed ML Pipelines](https://mlsanity.com/paper/2604.19468/) (2026)
- [Fairness Theatre: Evaluating Post-Hoc Fairness Interventions in Vendor-Controlled Early Warning Systems](https://mlsanity.com/paper/2609.38552/) (2026)

## Abstract (arXiv)

The use of algorithms for decision-making in higher education is steadily growing, promising cost-savings to institutions and personalized service for students but also raising ethical challenges around surveillance, fairness, and interpretation of data. To address the lack of systematic understanding of how these algorithms are currently designed, we reviewed an extensive corpus of papers proposing algorithms for decision-making in higher education. We categorized them based on input data, computational method, and target outcome, and then investigated the interrelations of these factors with the application of human-centered lenses: theoretical, participatory, or speculative design. We found that the models are trending towards deep learning, and increased use of student personal data and protected attributes, with the target scope expanding towards automated decisions. However, despite the associated decrease in interpretability and explainability, current development predominantly fails to incorporate human-centered lenses. We discuss the challenges with these trends and advocate for a human-centered approach.