# GAM Coach: Towards Interactive and User-centered Algorithmic Recourse

## TL;DR

GAM Coach is an interactive, user-centered system that helps end users generate personalized, actionable recourse plans for machine learning models, enhancing transparency and understanding through visualizations.

## Contribution

The paper introduces GAM Coach, an open-source tool that combines integer linear programming with interactive visualizations to enable personalized recourse generation for GAMs.

## Key findings

- Users find GAM Coach usable and useful.
- Personalized recourse plans are preferred over generic ones.
- Transparency increases opportunities for users to discover model patterns.

## Abstract

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/.