# AI and Holistic Review: Informing Human Reading in College Admissions

arXiv: https://arxiv.org/abs/1912.09318 · HTML: https://mlsanity.com/paper/1912.09318/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Dec 2019.

## Built on by (most cited first, 11 of 11)

- [FairAIED: Navigating Fairness, Bias, and Ethics in Educational AI Applications](https://mlsanity.com/paper/2407.18745/) (2024)
- [Towards Equity and Algorithmic Fairness in Student Grade Prediction](https://mlsanity.com/paper/2105.06604/) (2021)
- [Enhancing Model Robustness and Fairness with Causality: A Regularization Approach](https://mlsanity.com/paper/2110.00911/) (2021)
- [Automating Data Annotation under Strategic Human Agents: Risks and Potential Solutions](https://mlsanity.com/paper/2405.08027/) (2024)
- [Poor Alignment and Steerability of Large Language Models: Evidence from College Admission Essays](https://mlsanity.com/paper/2503.20062/) (2025)
- [Evaluation of group fairness measures in student performance prediction problems](https://mlsanity.com/paper/2208.10625/) (2022)
- [Augmenting Holistic Review in University Admission using Natural Language Processing for Essays and Recommendation Letters](https://mlsanity.com/paper/2306.17575/) (2023)
- [Admission Prediction in Undergraduate Applications: an Interpretable Deep Learning Approach](https://mlsanity.com/paper/2401.11698/) (2024)
- [Bias Analysis of AI Models for Undergraduate Student Admissions](https://mlsanity.com/paper/2412.02528/) (2024)
- [Learning Fair Representations with Kolmogorov-Arnold Networks](https://mlsanity.com/paper/2511.11767/) (2025)
- [Individual Fairness In Strategic Classification](https://mlsanity.com/paper/2602.05084/) (2026)

## Abstract (arXiv)

College admissions in the United States is carried out by a human-centered method of evaluation known as holistic review, which typically involves reading original narrative essays submitted by each applicant. The legitimacy and fairness of holistic review, which gives human readers significant discretion over determining each applicant's fitness for admission, has been repeatedly challenged in courtrooms and the public sphere. Using a unique corpus of 283,676 application essays submitted to a large, selective, state university system between 2015 and 2016, we assess the extent to which applicant demographic characteristics can be inferred from application essays. We find a relatively interpretable classifier (logistic regression) was able to predict gender and household income with high levels of accuracy. Findings suggest that data auditing might be useful in informing holistic review, and perhaps other evaluative systems, by checking potential bias in human or computational readings.