Neural Networks are ubiquitous in high energy physics research. However, these highly nonlinear parameterized functions are treated as \textit{black boxes}- whose inner workings to convey information and build the desired input-output relationship are often intractable. Explainable AI (xAI) methods can be useful in determining a neural model's relationship with data toward making it \textit{interpretable} by establishing a quantitative and tractable relationship between the input and the model's output. In this letter of interest, we explore the potential of using xAI methods in the context of problems in high energy physics.
@article{arxiv.2206.06632,
title = {Explainable AI for High Energy Physics},
author = {Mark S. Neubauer and Avik Roy},
journal= {arXiv preprint arXiv:2206.06632},
year = {2022}
}