Designing for the Last Mile in Machine Learning

Machine learning is now a general-purpose technology. In many domains, we can build models to support important decisions or automate routine tasks. Yet we may not reap their benefits due to disuse, or even inflict harm due to misuse. In this talk, I will present methodological advances that address these "last mile" challenges. First, I will describe a method to learn simple risk scores that are readily adopted for medical decision support, and discuss its applications to adult ADHD screening and ICU seizure prediction. Next, I will describe how machine learning models may harm individuals in consumer-facing applications by violating their right to autonomy. I will then introduce the notion of “recourse” and formalize methods to prevent such harms without interfering in model development.