Algorithmic predictions are increasingly used to inform the allocations of goods and services in the public sphere. In these domains, predictions serve as a means to an end. They provide stakeholders with insights into the likelihood of future events in order to improve decision making quality, and enhance social welfare. However, if maximizing welfare is the question, to what extent is improving prediction the best answer?

In this talk, we discuss various attempts to contextualize the relative value of algorithmic predictions through both theory and practice. The goal of the first part will be to formally understand how the welfare benefits of improving prediction compare to those of expanding access when distributing social goods. In the latter half, an empirical case study will be presented illustrating how these issues play out in the context of a risk prediction system used throughout Wisconsin public schools.

Bio:

Juan Carlos Perdomo is currently a postdoctoral fellow at Harvard University’s Center for Research on Computation and Society. His research centers on the theoretical and empirical foundations of machine learning. Perdomo is particularly interested in studying the downstream consequences, and feedback loops, that arise when predictions are used to make decisions about people.

Perdomo received his Ph.D. from the University of California, Berkeley and his bachelor’s degree in computer science and math from Harvard College.

Contributions to and/or sponsorship of any event does not constitute departmental or institutional endorsement of the specific program, speakers or views presented.

If you need an accommodation for a disability please contact Jean Butcher at  at least one week before the event.