mlsanityData to 14 Oct 2026

GAM Changer: Editing Generalized Additive Models with Interactive Visualization

arXiv 2112.03245 ↗First posted 6 Dec 2021cs.LG cs.AI cs.HCcited by 18

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Abstract (arXiv)

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 https://interpret.ml/gam-changer.

Key facts. Cited by 18 papers in the map (data through 14 Oct 2026). Builds on 15 of its 34 references that are in the map. First posted on arXiv 6 Dec 2021.

Builds on

15 of 34 references.

References · 15

  1. 2017A Unified Approach to Interpreting Model Predictionscited by 6,555
  2. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,166
  3. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,070
  4. 2021Interpretable Machine Learning: Fundamental Principles and 10 Grand Challengescited by 367
  5. 2020Neural Additive Models: Interpretable Machine Learning with Neural Netscited by 319
  6. 2020Understanding the Role of Individual Units in a Deep Neural Networkcited by 255
  7. 2019InterpretML: A Unified Framework for Machine Learning Interpretabilitycited by 246
  8. 2018Identifying and Controlling Important Neurons in Neural Machine Translationcited by 111
  9. 2020General Pitfalls of Model-Agnostic Interpretation Methods for Machine Learning Modelscited by 75
  10. 2020Human Factors in Model Interpretability: Industry Practices, Challenges, and Needscited by 74
  11. 2020How do Data Science Workers Collaborate? Roles, Workflows, and Toolscited by 64
  12. 2015Interpretable Classification Models for Recidivism Predictioncited by 44
3 more
  1. 2020How Interpretable and Trustworthy are GAMs?cited by 44
  2. 2020Finding Experts in Transformer Modelscited by 28
  3. 2021Dodrio: Exploring Transformer Models with Interactive Visualizationcited by 28

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 5; 2023: 4; 2024: 3; 2025: 4; 2026: 2.

18 papers.

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  1. 2024Amazing Things Come From Having Many Good Modelscited by 55
  2. 2023Exploring and Interacting with the Set of Good Sparse Generalized Additive Modelscited by 30
  3. 2022Interpretability, Then What? Editing Machine Learning Models to Reflect Human Knowledge and Valuescited by 25
  4. 2022Data-Efficient and Interpretable Tabular Anomaly Detectioncited by 15
  5. 2024Contrastive Explanations That Anticipate Human Misconceptions Can Improve Human Decision-Making Skillscited by 14
  6. 2022TimberTrek: Exploring and Curating Sparse Decision Trees with Interactive Visualizationcited by 13
  7. 2022Perspectives on Incorporating Expert Feedback into Model Updatescited by 7
  8. 2023LLMs Understand Glass-Box Models, Discover Surprises, and Suggest Repairscited by 7
  9. 2022StickyLand: Breaking the Linear Presentation of Computational Notebookscited by 6
  10. 2023Missing Values and Imputation in Healthcare Data: Can Interpretable Machine Learning Help?cited by 6
  11. 2024Explainable AI needs formalizationcited by 5
  12. 2025How to Enable Effective Cooperation Between Humans and NLP Models: A Survey of Principles, Formalizations, and Beyondcited by 4
6 more
  1. 2023FeedbackLogs: Recording and Incorporating Stakeholder Feedback into Machine Learning Pipelinescited by 3
  2. 2025Interpretable Clinical Classification with Kolmogorov-Arnold Networkscited by 3
  3. 2026Selecting Feature Interactions for Generalized Additive Models by Distilling Foundation Modelscited by 2
  4. 2026Why are there many equally good models? An Anatomy of the Rashomon Effectcited by 1
  5. 2025VAR: Visual Analysis for Rashomon Set of Machine Learning Models' Performancecited by 0
  6. 2025Transparent and Fair Profiling in Employment Services: Evidence from Switzerlandcited by 0