Explainable AI in Manufacturing: an Analysis of Transparency and Interpretability Methods for the XMANAI Platform
Rui Branco, Carlos Agostinho, Sergio Gusmeroli, Eleni Lavasa, Zoumpolia Dikopoulou, David Monzó, Fenareti Lampathaki · 2023
The use of artificial intelligence (AI) in manufacturing has become increasingly common, but the lack of transparency and interpretability of AI models can limit their adoption in critical applications, due to lack of human understanding. Explainable AI (XAI) has emerged as a solution to this problem by providing insights into the decision-making process of AI models. This paper analyses different approaches for AI transparency and interpretability, starting from explainability by-design to post-hoc explainability, pointing out trade-offs, advantages and disadvantages of each one, as well as providing an overview of different applications in manufacturing processes. The paper concludes presenting XMANAI as an innovative platform for manufacturing users to develop insightful XAI pipelines that can assist in their everyday operations and decision-making. The comprehensive overview of methods here presented serves as the ground basis for the platform draft catalogue of XAI models.