Explaining high-dimensional text classifiers
Odelia Melamed Rich Caruana
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.