Welcome! You are invited to join a meeting: Conference: Scoring Systems: At the Extreme of Interpretable Machine Learning. After registering, you will receive a confirmation email about joining the meeting.

This conference is presented as part of the Montreal Speaker Series in the Ethics of AI. SPEAKER Cynthia Rudin Professor of computer science, electrical and computer engineering, statistical science, and biostatistics & bioinformatics at Duke University With widespread use of machine learning, there have been serious societal consequences from using black box models for high-stakes decisions, including flawed bail and parole decisions in criminal justice, flawed models in healthcare, and black box loan decisions in finance. Interpretability of machine learning models is critical in high stakes decisions. In this talk, I will focus on one of the most fundamental and important problems in the field of interpretable machine learning: optimal scoring systems. Scoring systems are sparse linear models with integer coefficients. Such models first started to be used ~100 years ago. Generally, such models are created without data, or are constructed by manual feature selection and rounding logistic regression coefficients, but these manual techniques sacrifice performance; humans are not naturally adept at high-dimensional optimization. I will present the first practical algorithm for building optimal scoring systems from data. This method has been used for several important applications to healthcare and criminal justice. More information: https://sites.google.com/view/dmartin/ai-ethics/speakers?#h.nihlg6vib2nz