Inequality
The MD4SG inequality working group studies how optimization, incentive design, and machine learning can mitigate or magnify social and economic inequality.
We are especially focused on provision and targeting of social programs: when and how should resources be directed specifically to the most vunlerable members of the population?
How should these individuals be selected? Beyond these core questions, our research interests are wide-ranging, including both the philosophical underpinnings of economic design and the practical realities of studying institutions that serve vulnerable populations.
Representative Projects
- Rediet Abebe, Christian Ikeokwu, and Sam Taggart. “Robust Welfare Guarantees for Decentralized Credit Organizations.” Workshop on Fair AI in Finance (NeurIPS '20), Workshop on AI for Social Good (IJCAI '21).
- Rediet Abebe, Jon Kleinberg, and S. Matthew Weinberg. “Subsidy Allocations in the Presence of Income Shocks.” Proc. of the 34th AAAI conference on Artificial Intelligence (AAAI '20).
- Maximilian Kasy and Rediet Abebe “Fairness, Equality, and Power in Algorithmic Decision-Making.” Workshop on Participatory Approaches to Machine Learning (ICML '20).
Working Group Organizers
| Samuel Taggart |
Assistant Professor in Computer Science |
Oberlin College |
Working Group Members