I am a PhD Candidate at Brown University advised by Seny Kamara and Suresh Venkatasubramanian. I’ve also held positions as a research intern at IBM Research, Adobe, and Arthur AI. Before graduate school, I studied Computer Science and Mathematics at the University of Maryland - College Park, where I worked with John Dickerson.
My research investigates foundational questions about uncertainty pertaining to responsible machine learning. Most often, I design and analyze algorithms that provably identify and mitigate issues surrounding data-driven decisions. Specifically, I use statistical techniques to examine the various sources of uncertainty which can obscure the responsible deployment of automated tools in real-world contexts. The high level goal of my research is to produce new ideas that can provide insight into existing and emerging challenges at the intersection of society and computation.
Machine Learning, Optimal Transport
Observing Context Improves Disparity Estimation when Race is Unobserved
Kweku Kwegyir-Aggrey, Naveen Durvasula, Jennifer Wang, Suresh Venkatasubramanian. Proceedings of AAAI Conference on AI, Ethics, and Society (AIES ‘24) [PDF].
The Misuse of AUC: What High Impact Risk Assessment Gets Wrong
Kweku Kwegyir-Aggrey, Marissa Gerchick, Malika Mohan, Aaron Horowitz, Suresh Venkatasubramanian. Proceedings of ACM Conference on Fairness, Accountability, and Transparency (FAccT ‘23) [PDF].
Model Selection’s Disparate Impact in Real-World Deep Learning Applications
Jessica Zosa Forde, A. Feder Cooper, Kweku Kwegyir-Aggrey, Christopher De Sa, Michael Littman. Science of Deep Learning Workshop (ICLR ‘21) [PDF].
Geometric Repair for Fair Classification at Any Decision Threshold
Kweku Kwegyir-Aggrey, Jessica Dai, A. Feder Cooper, Keegan Hines. The Workshop on Artificial Intelligence for Social Good (AAAI ‘23) [PDF].
Powered by Jekyll and Minimal Light theme.