Description: ML-IRL will focus on the challenges of real-world use of machine learning and the gap between what ML can do in theory and what is needed in practice. Given the tremendous recent advances in methodology from causal inference to deep learning, the strong interest in applications (in health, climate and beyond), and discovery of problematic implications (e.g. issues of fairness and explainability) now is an ideal time to examine how we develop, evaluate and deploy ML and how we can do it better. We envision a workshop that is focused on productive solutions, not mere identification of problems or demonstration of failures.

Overall, we aim to examine how real-world applications can and should influence every stage of ML, from how we develop algorithms to how we evaluate them. These topics are fundamental for the successful real-world use of ML, but are rarely prioritized. We believe that a workshop focusing on these issues in a domain independent way is a necessary starting point for building more useful and usable ML. We will have speakers and participants representing all core topics (developing novel algorithms that work in the real world, specific applications and how we can learn from them, human factors and fairness) and bringing experience in true real-world deployments (e.g. fighting poverty with data, clinical trials). In addition to building a community and awareness of the research gap, ML-IRL will produce a whitepaper with key open problems and a path toward solutions.