Date: Wednesday, April 20th, 2022
9:00 am – 10:00 am Pacific Time
12:00 pm – 1:00 pm Eastern Time
Location: Weekly Seminar, Zoom
I will speak about the need for considering dynamic changes to the environment in response to allocational policies. As an example, I will introduce a sequential model for allocating opportunities, such as higher education, in a society that exhibits bottlenecks in socio-economic mobility. I will discuss how the problem of optimal allocation reflects a trade-off between the benefits conferred by the opportunities in the current generation and the potential to elevate the socioeconomic status of recipients, shaping the composition of future generations in ways that can benefit further from the opportunities. Our results show how optimal allocations in this model arise as solutions to continuous optimization problems over multiple generations, and in general, these optimal solutions can favor recipients of low socioeconomic status over slightly higher-performing individuals of high socioeconomic status — a form of socioeconomic affirmative action that the society in our model discovers in the pursuit of purely payoff-maximizing goals. I will conclude with directions for future work.
Hoda Heidari is an Assistant Professor in Machine Learning and Societal Computing at the School of Computer Science, Carnegie Mellon University. Her research is broadly concerned with the social, ethical, and economic implications of Artificial Intelligence. In particular, her research addresses issues of unfairness and opaqueness through Machine Learning. Her work in this area has won a best-paper award at the ACM Conference on Fairness, Accountability, and Transparency (FAccT) and an exemplary track award at the ACM Conference on Economics and Computation (EC). She has organized several scholarly events on topics related to Responsible and Trustworthy AI, including a tutorial at the Web Conference (WWW) and several workshops at the Neural and Information Processing Systems (NeurIPS) conference and the International Conference on Learning Representations (ICLR). Dr. Heidari completed her doctoral studies in Computer and Information Science at the University of Pennsylvania. She holds an M.Sc. degree in Statistics from the Wharton School of Business. Before joining Carnegie Mellon as a faculty member, she was a postdoctoral scholar at the Machine Learning Institute of ETH Zürich, followed by a year at the Artificial Intelligence, Policy, and Practice (AIPP) initiative at Cornell University.