# GAM(e) changer or not? An evaluation of interpretable machine learning models based on additive model constraints

arXiv: https://arxiv.org/abs/2204.09123 · HTML: https://mlsanity.com/paper/2204.09123/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 71 references that are in the map.
- First posted on arXiv 19 Apr 2022.

## Builds on (most cited first, 19 of 19)

- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [Neural Additive Models: Interpretable Machine Learning with Neural Nets](https://mlsanity.com/paper/2004.13912/) (2020)
- [InterpretML: A Unified Framework for Machine Learning Interpretability](https://mlsanity.com/paper/1909.09223/) (2019)
- [Techniques for Interpretable Machine Learning](https://mlsanity.com/paper/1808.00033/) (2018)
- [GAMI-Net: An Explainable Neural Network based on Generalized Additive Models with Structured Interactions](https://mlsanity.com/paper/2003.07132/) (2020)
- [How Interpretable and Trustworthy are GAMs?](https://mlsanity.com/paper/2006.06466/) (2020)
- [Explainable Neural Networks based on Additive Index Models](https://mlsanity.com/paper/1806.01933/) (2018)
- [Unwrapping The Black Box of Deep ReLU Networks: Interpretability, Diagnostics, and Simplification](https://mlsanity.com/paper/2011.04041/) (2020)
- [Enhancing Explainability of Neural Networks through Architecture Constraints](https://mlsanity.com/paper/1901.03838/) (2019)
- [Deep learning in business analytics and operations research: Models, applications and managerial implications](https://mlsanity.com/paper/1806.10897/) (2018)
- [Forecasting remaining useful life: Interpretable deep learning approach via variational Bayesian inferences](https://mlsanity.com/paper/1907.05146/) (2019)
- [A systematic comparison of supervised classifiers](https://mlsanity.com/paper/1311.0202/) (2013)
- [Local Post-Hoc Explanations for Predictive Process Monitoring in Manufacturing](https://mlsanity.com/paper/2009.10513/) (2020)
- [Critical Empirical Study on Black-box Explanations in AI](https://mlsanity.com/paper/2109.15067/) (2021)
- [A Technique for Determining Relevance Scores of Process Activities using Graph-based Neural Networks](https://mlsanity.com/paper/2008.03110/) (2020)
- [Intelligent Decision Assistance Versus Automated Decision-Making: Enhancing Knowledge Work Through Explainable Artificial Intelligence](https://mlsanity.com/paper/2109.13827/) (2021)

## Built on by (most cited first, 6 of 6)

- [Generative AI](https://mlsanity.com/paper/2309.07930/) (2023)
- [Meta-learning Loss Functions of Parametric Partial Differential Equations Using Physics-Informed Neural Networks](https://mlsanity.com/paper/2412.00225/) (2024)
- [A machine learning framework for interpretable predictions in patient pathways: The case of predicting ICU admission for patients with symptoms of sepsis](https://mlsanity.com/paper/2405.13187/) (2024)
- [Estimate Deformation Capacity of Non-Ductile RC Shear Walls using Explainable Boosting Machine](https://mlsanity.com/paper/2301.04652/) (2023)
- [IGANN Sparse: Bridging Sparsity and Interpretability with Non-linear Insight](https://mlsanity.com/paper/2403.11363/) (2024)
- [Quantifying Visual Properties of GAM Shape Plots: Impact on Perceived Cognitive Load and Interpretability](https://mlsanity.com/paper/2409.16870/) (2024)

## Abstract (arXiv)

The number of information systems (IS) studies dealing with explainable artificial intelligence (XAI) is currently exploding as the field demands more transparency about the internal decision logic of machine learning (ML) models. However, most techniques subsumed under XAI provide post-hoc-analytical explanations, which have to be considered with caution as they only use approximations of the underlying ML model. Therefore, our paper investigates a series of intrinsically interpretable ML models and discusses their suitability for the IS community. More specifically, our focus is on advanced extensions of generalized additive models (GAM) in which predictors are modeled independently in a non-linear way to generate shape functions that can capture arbitrary patterns but remain fully interpretable. In our study, we evaluate the prediction qualities of five GAMs as compared to six traditional ML models and assess their visual outputs for model interpretability. On this basis, we investigate their merits and limitations and derive design implications for further improvements.