Related papers: Verifiable affirmative action in school admissions
Each year, selective American colleges sort through tens of thousands of applications to identify a first-year class that displays both academic merit and diversity. In the 2023-2024 admissions cycle, these colleges faced unprecedented…
Actionable recourse studies whether individuals can modify feasible features to overturn unfavorable outcomes produced by AI-assisted decision-support systems. However, many such systems operate in competitive settings, such as admission or…
Until 2014, admissions to the Indian Institutes of Technology (IITs) were conducted under one umbrella, whereas the admissions to the non-IIT Centrally Funded Government Institutes (CFTIs) were conducted under a different umbrella, the…
Decision makers sometimes cannot observe the consequences of their actions ex-post. This paper axiomatically characterizes a decision model in which the decision maker cares about verifying that a good consequence has been achieved.…
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…
Over the past two decades, the notion of implicit bias has come to serve as an important component in our understanding of discrimination in activities such as hiring, promotion, and school admissions. Research on implicit bias posits that…
India has enacted an intricate affirmative action program through a reservation system since the 1950s. Notably, in 2008, a historic judgment by the Supreme Court of India (SCI) in the case of Ashoka Kumar Thakur vs. Union of India mandated…
Fair representation learning provides an effective way of enforcing fairness constraints without compromising utility for downstream users. A desirable family of such fairness constraints, each requiring similar treatment for similar…
Machine learning models are often used to decide who receives a loan, a job interview, or a public benefit. Models in such settings use features without considering their actionability. As a result, they can assign predictions that are…
In a 3-2 split verdict, the Supreme Court approved the exclusion of India's socially and economically backward classes from its affirmative action measures to address economic deprivation. Dissenting justices, including the Chief Justice of…
Inferring applicant preferences is fundamental in many analyses of school-choice data. Application mistakes make this task challenging. We propose a novel approach to deal with the mistakes in a deferred-acceptance matching environment. The…
Many real matching markets encounter distributional and fairness constraints. Motivated by the Chinese Major Transition Program (CMT), this paper studies the design of exchange mechanisms within a fresh framework of both distributional and…
We extend Approval voting to the settings where voters may have intransitive preferences. The major obstacle to applying Approval voting in these settings is that voters are not able to clearly determine who they should approve or…
In many machine learning applications, there are multiple decision-makers involved, both automated and human. The interaction between these agents often goes unaddressed in algorithmic development. In this work, we explore a simple version…
Abstaining classifiers have the option to abstain from making predictions on inputs that they are unsure about. These classifiers are becoming increasingly popular in high-stakes decision-making problems, as they can withhold uncertain…
Proposition. Let $f$ be a predictor trained on a distribution $P$ and evaluated on a shifted distribution $Q$. Under verifiable regularity and complexity constraints, the excess risk under shift admits an explicit upper bound determined by…
I study the optimal design of ratings to motivate agent investment in quality when transfers are unavailable. The principal designs a rating scheme that maps the agent's quality to a (possibly stochastic) score. The agent has private…
We present Phrase-Verified Voting, a voter-verifiable remote voting system assembled from commercial off-the-shelf software for small private elections. The system is transparent and enables each voter to verify that the tally includes…
As machine learning systems are increasingly used to make real world legal and financial decisions, it is of paramount importance that we develop algorithms to verify that these systems do not discriminate against minorities. We design a…
In this study, we consider the real-world problem of assigning students to classes, where each student has a preference list, ranking a subset of classes in order of preference. Though we use existing approaches to include the daily class…