Min Wu, Dana Dachman-Soled, Furong Huang, PI (Computer Science)

Funding Agency

National Science Foundation and Amazon




This is a $625K, three-year grant. | NSF grant page |

Machine learning systems have become prominent in many applications in everyday life, such as healthcare, finance, hiring, and education. These systems are intended to improve upon human decision-making by finding patterns in massive amounts of data, beyond what can be intuited by humans. However, it has been demonstrated that these systems learn and propagate similar biases present in human decision-making. This project aims to develop general theory and techniques on fairness in AI, with applications to improving retention and graduation rates of under-represented groups in STEM graduate programs. Recent research has shown that simply focusing on admission rates is not sufficient to improve graduation rates. This project is envisioned to go beyond designing "fair classifiers" such as fair graduate admission that satisfy a static fairness notion in a single moment in time, and designs AI systems that make decisions over a period of time with the goal of ensuring overall long-term fair outcomes at the completion of a process. The use of data-driven AI solutions can allow the detection of patterns missed by humans, to empower targeted intervention and fair resource allocation over the course of an extended period of time. The research from this project will contribute to reducing bias in the admissions process and improving completion rates in graduate programs as well as fair decision-making in general applications of machine learning.

This project will focus on machine learning algorithms for resource allocation, which can be used at various points throughout a process such as in education. The team will propose new notions of fairness and show the applicability of those notions to settings in which limited resources, such as acceptance to the program, faculty mentoring, professional development, and paid assistantships or fellowships, are allocated to students fairly. The proposed research will also go beyond fairness in task-specific supervised learning settings and investigate fairness in unsupervised learning that guarantees to learn fair representations or generative models for multiple downstream tasks. The team will address the practical problems that arise due to uncongenial data in real-world sequential decision-making systems, including distribution shifts between training and test, imbalanced data, and missing sensitive attributes.