Collective Altruism in Recommender Systems

Ekaterina (Kat) Fedorova from MIT EECS joins us to discuss strategic learning in recommender systems—what happens when users collectively coordinate to game recommendation algorithms. Kat's research reveals surprising findings: algorithmic "protest movements" can paradoxically help platforms by providing clearer preference signals, and the challenge of distinguishing coordinated behavior from bot activity is more complex than it appears. This episode explores the intersection of machine learning and game theory, examining what happens when your training data actively responds to your algorithm.

Guest

Ekaterina (Kat) Fedorova: About Hello! I’m Ekaterina (or Kat), an NSF CSGrad4US fellow and an EECS PhD student at MIT. At MIT, I currently work Dr. Chara Podimata and Dr. Costis Daskalakis. I’m interested in a combination of theoretical (CS and micro econ) algorithmic game theory topics like strategic classification, no-regret algorithms, and learning more broadly. More concretely, I’m currently working on projects that involve Correlated agent strategies in strategic learning settings Allocation mechanisms to information-disparate strategic agents Learning from biased data and truncated statistics I spent the first year of my PhD at UPenn CS (where I also worked closely with Dr. Danaë Metaxa) before moving to MIT EECS to continue my work. Before my time as a PhD student, I worked as a full time researcher for Dr. Anna Costello at UChicago. I received a B.A. from UC Berkeley where I studied statistics and economics. In my free time, I like to try to spot opossums around my campus and try new teas