A Review of Explainable Artificial Intelligence in Manufacturing

• URL: http://arxiv.org/abs/2107.02295v1
• Date: Mon, 5 Jul 2021 21:59:55 GMT
• Title: A Review of Explainable Artificial Intelligence in Manufacturing
• Authors: Georgios Sofianidis, Jo\v{z}e M. Ro\v{z}anec, Dunja Mladeni\'c, Dimosthenis Kyriazis
• Abstract summary: The implementation of Artificial Intelligence (AI) systems in the manufacturing domain enables higher production efficiency, outstanding performance, and safer operations. Despite the high accuracy of these models, they are mostly considered black boxes: they are unintelligible to the human. We present an overview of Explainable Artificial Intelligence (XAI) techniques as a means of boosting the transparency of models.
• Score: 0.8793721044482613
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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