RSVP to attend the program.

Speaker: Kristina Lerman, Professor of Informatics, Indiana University

In a world flooded with information, we rely on social cues (what’s popular, who’s reputable) and algorithmic recommendations to find what to read, watch or cite. When these filters interact with our cognitive biases, they create feedback loops that decouple item popularity from quality, weakening collective discovery. 

In this talk, Kristina Lerman will present empirical evidence from two domains. First, online choice experiments reveal that attentional biases, reinforced by ranking algorithms, reward the most visible items, so that the best items may not become the most popular. Second, large-scale analyses of bibliometric data reveal how science “finds” good ideas and people. A “rich get richer” dynamic in science (aka the Matthew effect) operates as a feedback loop, bringing more attention to the already-recognized papers and scholars. This dynamic magnifies existing social biases tied to gender and prestige, creating disparities that disadvantage women scholars and researchers with less-prestigious affiliations. Together, these results show how algorithms and cognitive heuristics interact to unintentionally tilt the playing field and distort discovery. To improve discovery and innovation, we need systems that counter these feedback loops and correct for individual biases.

This talk is offered both in person and online. Light refreshments will be served.

Kristina Lerman is a Professor of Informatics at the Indiana University’s Luddy School of Informatics, Computing and Engineering. Previously, she spent 27 years at the University of Southern California, serving as a Senior Principal Scientist at USC Information Sciences Institute. Trained as a physicist, she applies machine learning and network science to questions in computational social science, examining how algorithms and platforms shape social behavior and access to information, attention and influence. Her work has been covered by The Washington Post, Wall Street Journal and The Atlantic. She is a fellow of the AAAI.