Interpretability and Explainability: A Machine Learning Zoo Mini-tour

In this literature review, we provided a survey of interpretable and explainable machine learning methods (see Tables 1 and 2 for the summary of the techniques), described commonest goals and desiderata for these techniques, motivated their relevance in several fields of application, and discussed their quantitative evaluation. Interpretability and explainability still remain an active area of research, especially, in the face of recent rapid progress in designing highly performant predictive models and inevitable infusion of machine learning into other domains, where decisions have far-reaching consequences. For years the field has been challenged by a lack of clear definitions for interpretability or explainability, these terms being often wielded "in a quasi-mathematical way"[6,122]. For many techniques, there still exist no satisfactory functionally-grounded evaluation criteria and universally accepted benchmarks, hindering reproducibility and model comparison. Moreover, meaningful adaptations of these methods to'real-world' machine learning systems and data analysis problems largely remain a matter for the future. It has been argued that, for successful and widespread use of interpretable and explainable machine learning models, stakeholders need to be involved in the discussion[4, 122]. A meaningful and equal collaboration between machine learning researchers and stakeholders from various domains, such as medicine, natural sciences, and law, is a logical next step within the evolution of interpretable and explainable ML.