Margaret Qingyang Wang
MIT EECS | Nadar Foundation Undergraduate Research and Innovation Scholar
Allocation Multiplicity and the Rashomon Set
2024–2025
Electrical Engineering and Computer Science
- AI and Machine Learning
Ashia Wilson
As algorithms increasingly determine access to valued resources and opportunities, we want to investigate whether introducing structured randomization into decisions can increase fairness while maintaining efficiency. We aim to develop a theoretical framework to study tradeoffs between decisions under uncertainty. This involves defining what fairness means for different stakeholders and what utility function the decision-maker is using. We will validate our framework with an empirical case study using real-world data. For example, exam school policies admit students based on a composite of their exam score, GPA, and socioeconomic status. We would see how randomization affects the balance of admitted students, and whether this would lead to a more “fair” outcome overall.
Through this SuperUROP, I’m hoping to explore algorithmic fairness in depth. I’ve previously worked on conducting black-box audits of recommendation algorithms and synthesizing global AI regulation and policies, and I hope to learn more about the field via this experience and from other members of the lab. I’m really excited by the chance to connect our research to a real-world case study.