Modern recommendation platforms have become complex, dynamic eco-systems. Platforms often rely on machine learning models to successfully match users to content, but most methods neglect to account for how they affect user behavior, satisfaction, and well-being of over time. Here we propose a novel dynamical-systems perspective to recommendation that allows to reason about, and control, macro-temporal aspects of recommendation policies as they relate to user behavior.
The task of optimizing machines to support human decision-making is often conflated with that of optimizing machines for accuracy, even though they are materially different. Whereas typical learning systems prescribe actions through prediction, our framework learns to to reframe problems in a way that directly supports human decisions. Using a novel human-in-the-loop training procedure, our framework learns problem representations that directly optimize human performance.
Machine learning has become imperative for informing decisions that affect the lives of humans across a multitude of domains. But when people benefit from certain predictive outcomes, they are prone to act strategically to improve those outcomes. Our goal in this project is to develop a practical learning framework that accounts for how humans behaviourally respond to classification rules. Our framework provides robustness while also providing means to promote favourable social outcomes.