Physics-Inspired Interpretability Of Machine Learning Models
This addresses the need for interpretability in sensitive areas like medicine and autonomous driving, offering a new method for feature identification, though it appears incremental as it adapts existing physical concepts to machine learning.
The paper tackles the problem of explaining decisions made by machine learning models by proposing a novel approach inspired by energy landscapes from physics to identify relevant input features that drive model decisions, demonstrating its applicability with synthetic and real-world examples to enhance interpretability.
The ability to explain decisions made by machine learning models remains one of the most significant hurdles towards widespread adoption of AI in highly sensitive areas such as medicine, cybersecurity or autonomous driving. Great interest exists in understanding which features of the input data prompt model decision making. In this contribution, we propose a novel approach to identify relevant features of the input data, inspired by methods from the energy landscapes field, developed in the physical sciences. By identifying conserved weights within groups of minima of the loss landscapes, we can identify the drivers of model decision making. Analogues to this idea exist in the molecular sciences, where coordinate invariants or order parameters are employed to identify critical features of a molecule. However, no such approach exists for machine learning loss landscapes. We will demonstrate the applicability of energy landscape methods to machine learning models and give examples, both synthetic and from the real world, for how these methods can help to make models more interpretable.