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DISSECT: Disentangled Simultaneous Explanations via Concept Traversals
[article]
2022
arXiv
pre-print
Explaining deep learning model inferences is a promising venue for scientific understanding, improving safety, uncovering hidden biases, evaluating fairness, and beyond, as argued by many scholars. One of the principal benefits of counterfactual explanations is allowing users to explore "what-if" scenarios through what does not and cannot exist in the data, a quality that many other forms of explanation such as heatmaps and influence functions are inherently incapable of doing. However, most
arXiv:2105.15164v4
fatcat:aymyl3j45rdcjisqy2oxld3yiy