The Science of the Predicted Human talk series: Keyon Vafa

The Pioneer Center for Artificial Intelligence, DTU Compute and SODAS are delighted to announce that we will be hosting a talk by Keyon Vafa (postdoctoral fellow at Harvard University) as part of the ‘The Science of the Predicted Human’ talk series. In his research, Keyon develops machine learning methodologies to uncover insights into human behavior in labor economics and political science, among other fields in the social sciences. Keyon will be presenting his interesting and highly innovative work (joint with Susan Athey and David Blei) on how to decompose the gender wage gap over worker careers using foundation models.


"Decomposing the Gender Wage Gap with a Foundation Model of Labor History"


A large literature in labor economics seeks to decompose gender wage gaps into different sources, including portions explained by cross-gender differences in education and occupation. While career histories contain valuable information about sources of gender wage disparities, they are too high-dimensional to include in standard econometric techniques. This talk presents new machine learning methods for decomposing gender wage gaps over worker careers. We develop a "foundation model" of career trajectories to summarize worker histories with low-dimensional representations. We show how to fine-tune the foundation model on small survey datasets while ensuring that the representations do not omit features of history whose exclusion would bias decompositions. On data from the Panel Study of Income Dynamics, we show that full worker history explains about 25% of the gender wage gap than is unexplained by standard summary statistics and covariates. We conclude by using the representations of worker history to identify clusters of history that are most important for explaining wage gaps.
Joint work with Susan Athey and David Blei.

The Predicted Human

Being human in 2023 implies being the target of a vast number of predictive infrastructures. In healthcare, algorithms predict not only potential pharmacological cures to disease but also their possible future incidence of those diseases. In governance, citizens are exposed to algorithms that predict - not only their day-to-day behaviors to craft better policy - but also to algorithms that attempt to predict, shape and manipulate their political attitudes and behaviors. In education, children’s emotional and intellectual development is increasingly the product of at-home and at-school interventions shaped around personalized algorithms. And humans worldwide are increasingly subject to advertising and marketing algorithms whose goal is to target them with specific products and ideas they will find palatable. Algorithms are everywhere – as are their intended as well as unintended consequences. The series is arranged with generous support by the Villum Foundation and the Pioneer Center for Artificial Intelligence.