AI Podcasts

Collective Altruism in Recommender Systems

In this episode, Data Skeptic talks with MIT PhD student Kat about how users can collectively manipulate recommender systems and why such coordinated actions can actually benefit the platforms. Kat explains her research that frames recommender algorithms as multi‑agent games, using game theory and matrix completion to model strategic user behavior beyond simple two‑player interactions. She highlights survey evidence showing that algorithmic activism—groups intentionally boosting or suppressing content—is more common than expected, and discusses the implications for algorithm design and fairness. The conversation also touches on how collaborative filtering creates incentives for users to act strategically, influencing both their own and others' recommendations.