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arXiv · 2311.13454

Explaining high-dimensional text classifiers

Odelia Melamed·Rich Caruana
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Abstract

Explainability has become a valuable tool in the last few years, helping humans better understand AI-guided decisions. However, the classic explainability tools are sometimes quite limited when considering high-dimensional inputs and neural network classifiers. We present a new explainability method using theoretically proven high-dimensional properties in neural network classifiers. We present two usages of it: 1) On the classical sentiment analysis task for the IMDB reviews dataset, and 2) our Malware-Detection task for our PowerShell scripts dataset.

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Odelia Melamed, Rich Caruana. 2023-11-22. Explaining high-dimensional text classifiers. https://arxiv.org/abs/2311.13454

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