Recently, artificial intelligence, especially machine learning has demonstrated remarkable performances in many tasks, from image processing to natural language processing, especially with the advent of deep learning. Along with research progress, machine learning has encroached into many different fields and disciplines. Some of them, such as the medical field, require high level of accountability, and thus transparency, which means we need to be able to explain machine decisions, predictions and justify their reliability. This requires greater interpretability, which often means we need to understand the mechanism underlying the algorithms. Unfortunately, the black-box nature of the deep learning is still unresolved, and many machine decisions are still poorly understood. We provide a review on interpretabilities suggested by different research works and categorize them, with the intention of providing alternative perspective that is hopefully more tractable for future adoption of interpretability standard. We explore further into interpretability in the medical field, illustrating the complexity of interpretability issue.
07/17/2019 ∙ by Erico Tjoa, et al. ∙ 37 ∙ share
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