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

Intrinsically interpretable models like GAMs offer a genuine alternative to "black box" AI: Rather than using post-hoc explanations that approximate how a model works after the fact, GAMs are designed to be transparent from the start, allowing stakeholders to see exactly how each input factor influences predictions without relying on approximations.

There's a trade-off between interpretability and prediction accuracy that organizations need to understand: The study compared five GAM variants against six traditional machine learning models, finding that the choice between them involves weighing how well you can understand the model's decisions against how accurately it makes predictions—a critical consideration for responsible AI deployment.

Visual outputs from interpretable models can make AI decisions accessible to non-technical stakeholders: GAMs generate shape functions and visual representations that show how different factors affect outcomes, potentially enabling business users, regulators, and affected individuals to understand AI decisions without requiring technical expertise.

Current XAI approaches may give false confidence in understanding AI systems: The paper cautions that most explainability techniques in use today provide only approximate explanations of underlying models, suggesting that organizations relying solely on these post-hoc methods may have incomplete or misleading understanding of how their AI actually works.

Intrinsically interpretable models need further development for practical use: The study identifies both merits and limitations of GAMs and calls for design improvements, indicating that while these models show promise for responsible AI, they are not yet a complete solution for all organizational needs.