# Trading off performance and human oversight in algorithmic policy: evidence from Danish college admissions

arXiv: https://arxiv.org/abs/2411.15348 · HTML: https://mlsanity.com/paper/2411.15348/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Nov 2024.

## Built on by (most cited first, 1 of 1)

- [Local Differences, Global Lessons: Insights from Organisation Policies for International Legislation](https://mlsanity.com/paper/2503.05737/) (2025)

## Abstract (arXiv)

Student dropout is a significant concern for educational institutions due to its social and economic impact, driving the need for risk prediction systems to identify at-risk students before enrollment. We explore the accuracy of such systems in the context of higher education by predicting degree completion before admission, with potential applications for prioritizing admissions decisions. Using a large-scale dataset from Danish higher education admissions, we demonstrate that advanced sequential AI models offer more precise and fair predictions compared to current practices that rely on either high school grade point averages or human judgment. These models not only improve accuracy but also outperform simpler models, even when the simpler models use protected sociodemographic attributes. Importantly, our predictions reveal how certain student profiles are better matched with specific programs and fields, suggesting potential efficiency and welfare gains in public policy. We estimate that even the use of simple AI models to guide admissions decisions, particularly in response to a newly implemented nationwide policy reducing admissions by 10 percent, could yield significant economic benefits. However, this improvement would come at the cost of reduced human oversight and lower transparency. Our findings underscore both the potential and challenges of incorporating advanced AI into educational policymaking.