Interpretable Machine Learning: Moving From Mythos to Diagnostics

Despite years of progress in the field of Interpretable Machine Learning (IML), a significant gap persists between the technical objectives targeted by researchers' methods and the high-level goals stated as consumers' use cases. To address this gap, we argue for the IML community to embrace a diagnostic vision for the field. Instead of viewing IML methods as a panacea for a variety of overly broad use cases, we emphasize the need to systematically connect IML methods to narrower --yet better defined-- target use cases. To formalize this vision, we propose a taxonomy including both methods and use cases, helping to conceptualize the current gaps between the two. Then, to connect these two sides, we describe a three-step workflow to enable researchers and consumers to define and validate IML methods as useful diagnostics. Eventually, by applying this workflow, a more complete version of the taxonomy will allow consumers to find relevant methods for their target use cases and researchers to identify motivating use cases for their proposed methods.