Ai+ Training

Recommendation systems occupy an expanding role in everyday decision making; they have the potential to influence product consumption, individuals' perceptions of the world, and life-altering medical and legal decisions. The data used to train and evaluate these systems is algorithmically confounded: users are already exposed to algorithmic recommendations, creating a feedback loop between human choices and the recommendation system.  Using simulations, we will demonstrate how using data confounded in this way can impact both individuals and the platform as a whole.