Good Ideas are Hard to Find: How Cognitive Biases and Algorithms Interact to Constrain Discovery

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.

This talk is offered both in person and online. Please RSVP. 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.