Related papers: Downstream Effects of Affirmative Action
Information frictions can harm the welfare of participants in two-sided matching markets. Consider a centralized admission, where colleges cannot observe students' preparedness for success in a particular major or degree program. Colleges…
Many previous causal inference studies require no interference, that is, the potential outcomes of a unit do not depend on the treatments of other units. However, this no-interference assumption becomes unreasonable when a unit interacts…
We study search, evaluation, and selection of candidates of unknown quality for a position. We examine the effects of "soft" affirmative action policies increasing the relative percentage of minority candidates in the candidate pool. We…
Deploying an algorithmically informed policy is a significant intervention in the structure of society. As is increasingly acknowledged, predictive algorithms have performative effects: using them can shift the distribution of social…
Peer grading systems work well only if users have incentives to grade truthfully. An example of non-truthful grading, that we observed in classrooms, consists in students assigning the maximum grade to all submissions. With a naive grading…
We present a stylized model with feedback loops for the evolution of a population's wealth over generations. Individuals have both talent and wealth: talent is a random variable distributed identically for everyone, but wealth is a random…
For many students, placement into developmental education becomes a self-fulfilling prophecy. Placing college students into developmental education significantly negatively impacts student attainment, student probability of passing, and…
We study the problem of fair cohort selection from an unknown population, with a focus on university admissions. We start with the one-shot setting, where the admission policy must be fixed in advance and remain transparent, before…
The use of algorithmic decision making systems in domains which impact the financial, social, and political well-being of people has created a demand for these decision making systems to be "fair" under some accepted notion of equity. This…
We develop an Empirical Bayes grading scheme that balances the informativeness of the assigned grades against the expected frequency of ranking errors. Applying the method to a massive correspondence experiment, we grade the racial biases…
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…
The field of fair machine learning aims to ensure that decisions guided by algorithms are equitable. Over the last decade, several formal, mathematical definitions of fairness have gained prominence. Here we first assemble and categorize…
Explicit and implicit bias clouds human judgement, leading to discriminatory treatment of minority groups. A fundamental goal of algorithmic fairness is to avoid the pitfalls in human judgement by learning policies that improve the overall…
Avoiding bias and understanding the real-world consequences of AI-supported decision-making are critical to address fairness and assign accountability. Existing approaches often focus either on technical aspects, such as datasets and…
This article offers a critical response to the preprint by Matheus et al. (2025), which evaluates the academic performance of students admitted through different entry routes at Sao Paulo State University. Although the dataset compiled by…
Fairness-aware learning studies the development of algorithms that avoid discriminatory decision outcomes despite biased training data. While most studies have concentrated on immediate bias in static contexts, this paper highlights the…
Progression and assessment rules are often treated as administrative details, yet they fundamentally shape who is allowed to remain in higher education, and on what terms. This article uses a calibrated agent-based model to examine how…
Peer grading systems aggregate noisy reports from multiple students to approximate a true grade as closely as possible. Most current systems either take the mean or median of reported grades; others aim to estimate students' grading…
Today, there is no clear legal test for regulating the use of variables that proxy for race and other protected classes and classifications. This Article develops such a test. Decision tools that use proxies are narrowly tailored when they…
We propose a framework to assess how to optimally sort and grade students of heterogenous ability. Potential employers face uncertainty regarding an individual's productive value. Knowing which school an individual went to is useful for two…