Considerations for Evaluation and Generalization in Interpretable Machine Learning

As machine learning systems become ubiquitous, there has been a surge of interest in interpretable machine learning: systems that provide explanation for their outputs. These explanations are often used to qualitatively assess other criteria such as safety or non-discrimination. However, despite the interest in interpretability, there is little consensus on what interpretable machine learning is and how it should be measured and evaluated. In this paper, we discuss a definitions of interpretability and describe when interpretability is needed (and when it is not). Finally, we talk about a taxonomy for rigorous evaluation, and recommendations for researchers. We will end with discussing open questions and concrete problems for new researchers.

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