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/.

翻译：机器学习（ML）补救技术越来越多地用于高风险领域，为最终用户提供改变ML预测的行动方案，但这些技术假设ML开发者理解哪些输入变量可以更改。然而，补救计划的可操作性是主观的，且很难完全匹配开发者的预期。我们提出GAM Coach，一个新颖的开源系统，它将整数线性规划适配于生成广义加性模型（GAM）的可定制反事实解释，并利用交互式可视化使最终用户能够迭代生成满足其需求的补救计划。一项包含41名参与者的定量用户研究表明，我们的工具既可用又有用，且用户更偏好个性化补救计划而非通用计划。通过日志分析，我们探索了用户如何发现满意的补救计划，并提供实证证据表明，透明度可以为日常用户创造更多发现机器学习模型中反直觉模式的机会。GAM Coach可从以下网址获取：https://poloclub.github.io/gam-coach/。