GAM Changer: Editing Generalized Additive Models with Interactive Visualization

crown jewel figure
GAM Changer empowers domain experts and data scientists to easily and responsibly align model behaviors with their domain knowledge and values, via direct manipulation of GAM model weights. For example, (A) the GAM Canvas enables doctors to interpolate the predicted risk of dying from pneumonia to match their domain knowledge of a gradual risk increase from age 81 to age 87. GAM Changer promotes accountable editing and elucidates potential tradeoffs induced by the edits. (B1) The Metric Panel provides real time feedback on model performance. (B2) The Feature Panel helps users identify characteristics of affected samples and promotes awareness of fairness issues. To enable reversible transparent model edits, (B3) the History Panel allows the doctor to compare and revert changes, as well as document their motivations and editing contexts.
Demo Video
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
GAM Changer: Editing Generalized Additive Models with Interactive Visualization
  title = {{{GAM Changer}}: {{Editing Generalized Additive Models}} with {{Interactive Visualization}}},
  shorttitle = {{{GAM Changer}}},
  author = {Wang, Zijie J. and Kale, Alex and Nori, Harsha and Stella, Peter and Nunnally, Mark and Chau, Duen Horng and Vorvoreanu, Mihaela and Vaughan, Jennifer Wortman and Caruana, Rich},
  year = {2021},
  month = dec,
  journal = {arXiv:2112.03245 [cs]},
  url = {}
  archiveprefix = {arXiv}