Designing AI Systems with Steerable Long-Term Dynamics

The feedback that users provide through their choices (e.g. clicks, purchases) is one of the most common types of data readily available for training autonomous systems, and it is widely used in online platforms. However, naively training systems based on choice data may only improve short-term engagement, but not the long-term sustainability of the platform. In this talk, I will discuss some of the pitfalls of engagement-maximization, and explore methods that allow us to supplement engagement with additional criteria that are not limited to individual action-response metrics. The goal is to give platform operators a new set of macroscopic interventions for steering the dynamics of the platform, providing a new level of abstraction that goes beyond the engagement with individual recommendations or rankings.

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Speaker Details

Thorsten Joachims is a Professor in the Department of Computer Science and in the Department of Information Science at Cornell University, and he is an Amazon Scholar. His research interests center on a synthesis of theory and system building in machine learning, with applications in information access, language technology, and recommendation. His past research focused on counterfactual and causal inference, learning to rank, structured output prediction, support vector machines, text classification, learning with preferences, and learning from implicit feedback. He is an ACM Fellow, AAAI Fellow, KDD Innovations Award recipient, and member of the ACM SIGIR Academy.