Machine Learning Interpretability: A Science rather than a tool
This conceptual shift could help researchers and practitioners address interpretability more systematically, though it is incremental as it builds on existing discussions without introducing new methods or data.
The paper argues that machine learning interpretability should be defined as a science focused on answering specific questions (statistical, causal, counterfactual) rather than as a collection of tools, proposing a question-based framework to deepen understanding of ML problems.
The term "interpretability" is oftenly used by machine learning researchers each with their own intuitive understanding of it. There is no universal well agreed upon definition of interpretability in machine learning. As any type of science discipline is mainly driven by the set of formulated questions rather than by different tools in that discipline, e.g. astrophysics is the discipline that learns the composition of stars, not as the discipline that use the spectroscopes. Similarly, we propose that machine learning interpretability should be a discipline that answers specific questions related to interpretability. These questions can be of statistical, causal and counterfactual nature. Therefore, there is a need to look into the interpretability problem of machine learning in the context of questions that need to be addressed rather than different tools. We discuss about a hypothetical interpretability framework driven by a question based scientific approach rather than some specific machine learning model. Using a question based notion of interpretability, we can step towards understanding the science of machine learning rather than its engineering. This notion will also help us understanding any specific problem more in depth rather than relying solely on machine learning methods.