01220nas a2200169 4500008004100000245006200041210006100103260001400164520066400178653000800842653002200850653001900872100002100891700002000912700002000932856009800952 2024 eng d00aError-margin Analysis for Hidden Neuron Activation Labels0 aErrormargin Analysis for Hidden Neuron Activation Labels bSpringer 3 a

Understanding how high-level concepts are represented with- in artificial neural networks is a fundamental challenge in the field of arti- ficial intelligence. While existing literature in explainable AI emphasizes the importance of labeling neurons with concepts to understand their functioning, they mostly focus on identifying what stimulus activates a neuron in most cases; this corresponds to the notion of recall in informa- tion retrieval. We argue that this is only the first-part of a two-part job; it is imperative to also investigate neuron responses to other stimuli, i.e., their precision. We call this the neuron label’s error margin.

10aCNN10aConcept Induction10aExplainable AI1 aDalal, Abhilekha1 aRayan, Rushrukh1 aHitzler, Pascal uhttps://daselab.cs.ksu.edu/publications/error-margin-analysis-hidden-neuron-activation-labels