  Recent strides in interpretable machine learning (ML) research reveal that
models exploit undesirable patterns in the data to make predictions, which
potentially causes harms in deployment. However, it is unclear how we can fix
these models. We present our ongoing work, GAM Changer, an open-source
interactive system to help data scientists and domain experts easily and
responsibly edit their Generalized Additive Models (GAMs). With novel
visualization techniques, our tool puts interpretability into action --
empowering human users to analyze, validate, and align model behaviors with
their knowledge and values. Built using modern web technologies, our tool runs
locally in users' computational notebooks or web browsers without requiring
extra compute resources, lowering the barrier to creating more responsible ML
models. GAM Changer is available at https://interpret.ml/gam-changer.
