Explaining black boxes with a SMILE: Statistical Model-agnostic Interpretability with Local Explanations
This addresses the need for better interpretability in ML to enhance trust, though it appears incremental as it builds on previous approaches.
The authors tackled the problem of trustworthiness in machine learning by proposing SMILE, a method that uses statistical distance measures to improve explainability for black-box models, achieving applicability across various input data domains.
Machine learning is currently undergoing an explosion in capability, popularity, and sophistication. However, one of the major barriers to widespread acceptance of machine learning (ML) is trustworthiness: most ML models operate as black boxes, their inner workings opaque and mysterious, and it can be difficult to trust their conclusions without understanding how those conclusions are reached. Explainability is therefore a key aspect of improving trustworthiness: the ability to better understand, interpret, and anticipate the behaviour of ML models. To this end, we propose SMILE, a new method that builds on previous approaches by making use of statistical distance measures to improve explainability while remaining applicable to a wide range of input data domains.