Poster
in
Workshop: Bridging the Gap: from Machine Learning Research to Clinical Practice
GAM Changer: Editing Generalized Additive Models with Interactive Visualization
Zijie Jay Wang · Harsha Nori · Duen Horng Chau · Jennifer Wortman Vaughan · Rich Caruana
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, this 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://r2c-submission.surge.sh.