  Machine learning (ML) recourse techniques are increasingly used in
high-stakes domains, providing end users with actions to alter ML predictions,
but they assume ML developers understand what input variables can be changed.
However, a recourse plan's actionability is subjective and unlikely to match
developers' expectations completely. We present GAM Coach, a novel open-source
system that adapts integer linear programming to generate customizable
counterfactual explanations for Generalized Additive Models (GAMs), and
leverages interactive visualizations to enable end users to iteratively
generate recourse plans meeting their needs. A quantitative user study with 41
participants shows our tool is usable and useful, and users prefer personalized
recourse plans over generic plans. Through a log analysis, we explore how users
discover satisfactory recourse plans, and provide empirical evidence that
transparency can lead to more opportunities for everyday users to discover
counterintuitive patterns in ML models. GAM Coach is available at:
https://poloclub.github.io/gam-coach/.
