A Review of Explainable Artificial Intelligence in Manufacturing
It tackles the problem of model opaqueness affecting trust in decision-making for manufacturing professionals, but it is incremental as it provides an overview rather than new methods.
The paper reviews Explainable Artificial Intelligence (XAI) techniques to address the black-box nature of AI models in manufacturing, analyzing evaluation metrics and application scenarios to enhance transparency and trust.
The implementation of Artificial Intelligence (AI) systems in the manufacturing domain enables higher production efficiency, outstanding performance, and safer operations, leveraging powerful tools such as deep learning and reinforcement learning techniques. Despite the high accuracy of these models, they are mostly considered black boxes: they are unintelligible to the human. Opaqueness affects trust in the system, a factor that is critical in the context of decision-making. We present an overview of Explainable Artificial Intelligence (XAI) techniques as a means of boosting the transparency of models. We analyze different metrics to evaluate these techniques and describe several application scenarios in the manufacturing domain.