Robustness of Explainable Artificial Intelligence in Industrial Process Modelling
Abstract
eXplainable Artificial Intelligence (XAI) aims at providing understandable explanations of black box models. In this paper, we evaluate current XAI methods by scoring them based on ground truth simulations and sensitivity analysis. To this end, we used an Electric Arc Furnace (EAF) model to better understand the limits and robustness characteristics of XAI methods such as SHapley Additive exPlanations (SHAP), Local Interpretable Model-agnostic Explanations (LIME), as well as Averaged Local Effects (ALE) or Smooth Gradients (SG) in a highly topical setting. These XAI methods were applied to various types of black-box models and then scored based on their correctness compared to the ground-truth sensitivity of the data-generating processes using a novel scoring evaluation methodology over a range of simulated additive noise. The resulting evaluation shows that the capability of the Machine Learning (ML) models to capture the process accurately is, indeed, coupled with the correctness of the explainability of the underlying data-generating process. We furthermore show the differences between XAI methods in their ability to correctly predict the true sensitivity of the modeled industrial process.
Cite
@article{arxiv.2407.09127,
title = {Robustness of Explainable Artificial Intelligence in Industrial Process Modelling},
author = {Benedikt Kantz and Clemens Staudinger and Christoph Feilmayr and Johannes Wachlmayr and Alexander Haberl and Stefan Schuster and Franz Pernkopf},
journal= {arXiv preprint arXiv:2407.09127},
year = {2024}
}
Comments
11 pages, 3 figures, accepted at the ICML'24 Workshop ML4LMS; updated with improved results and fixed typos