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The Doctor Just Won't Accept That!
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Calls to arms to build interpretable models express a well-founded discomfort with machine learning. Should a software agent that does not even know what a loan is decide who qualifies for one? Indeed, we ought to be cautious about injecting machine learning (or anything else, for that matter) into applications where there may be a significant risk of causing social harm. However, claims that stakeholders "just won't accept that!" do not provide a sufficient foundation for a proposed field of study. For the field of interpretable machine learning to advance, we must ask the following questions: What precisely won't various stakeholders accept? What do they want? Are these desiderata reasonable? Are they feasible? In order to answer these questions, we'll have to give real-world problems and their respective stakeholders greater consideration.
Forward citations
Cited by 3 Pith papers
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Pragmatic Policy Development via Interpretable Behavior Cloning
A decision-tree behavior cloning approach that chooses the most frequent treatments per patient state yields interpretable policies with reliable off-policy evaluation in two clinical datasets.
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From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection
An inherently interpretable image classifier that selects sparse, semantically meaningful superpixel regions per instance and adaptively determines how many regions to keep based on prediction confidence.
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Unexplainability and Incomprehensibility of Artificial Intelligence
Advanced AI systems are unexplainable in full and produce explanations that humans cannot comprehend.
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