  Machine learning (ML) interpretability techniques can reveal undesirable
patterns in data that models exploit to make predictions--potentially causing
harms once deployed. However, how to take action to address these patterns is
not always clear. In a collaboration between ML and human-computer interaction
researchers, physicians, and data scientists, we develop GAM Changer, the first
interactive system to help domain experts and data scientists easily and
responsibly edit Generalized Additive Models (GAMs) and fix problematic
patterns. With novel interaction techniques, our tool puts interpretability
into action--empowering users to analyze, validate, and align model behaviors
with their knowledge and values. Physicians have started to use our tool to
investigate and fix pneumonia and sepsis risk prediction models, and an
evaluation with 7 data scientists working in diverse domains highlights that
our tool is easy to use, meets their model editing needs, and fits into their
current workflows. Built with modern web technologies, our tool runs locally in
users' web browsers or computational notebooks, lowering the barrier to use.
GAM Changer is available at the following public demo link:
https://interpret.ml/gam-changer.
