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What Does Explainable AI Really Mean? A New Conceptualization of Perspectives
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We characterize three notions of explainable AI that cut across research fields: opaque systems that offer no insight into its algo- rithmic mechanisms; interpretable systems where users can mathemat- ically analyze its algorithmic mechanisms; and comprehensible systems that emit symbols enabling user-driven explanations of how a conclusion is reached. The paper is motivated by a corpus analysis of NIPS, ACL, COGSCI, and ICCV/ECCV paper titles showing differences in how work on explainable AI is positioned in various fields. We close by introducing a fourth notion: truly explainable systems, where automated reasoning is central to output crafted explanations without requiring human post processing as final step of the generative process.
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Cited by 3 Pith papers
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A New Technique for AI Explainability using Feature Association Map
FAMeX introduces a graph-theoretic Feature Association Map to explain feature importance in AI classification models and outperforms PFI and SHAP on eight benchmarks.
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A New Technique for AI Explainability using Feature Association Map
FAMeX creates a graph of feature associations to explain AI classification decisions and outperforms SHAP and permutation feature importance on eight benchmark datasets.
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A New Technique for AI Explainability using Feature Association Map
FAMeX is a graph-based XAI algorithm claimed to outperform PFI and SHAP at gauging feature importance for classification.
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