Why Industry 5.0 Needs XAI 2.0?

• Type: Article in International Conference Proceedings Book
• Date: 2023

Authors

Bobek, S (Author) Other; Nowaczyk, S (Author) Other; João Gama (Author) FEP; Pashami, S (Author) Other; Rita Ribeiro (Author) FCUP; Taghiyarrenani, Z (Author) Other; Veloso, B (Author) FEP; Rajaoarisoa, LH (Author) Other; Szelazek, M (Author) Other; Nalepa, GJ (Author) Other

Conference proceedings International

Title: Joint Proceedings of the xAI-2023 Late-breaking Work, Demos and Doctoral Consortium co-located with the 1st World Conference on eXplainable Artificial Intelligence (xAI-2023), Lisbon, Portugal, July 26-28, 2023.

Pages: 1-6

Joint 1st World Conference on eXplainable Artificial Intelligence: Late-Breaking Work, Demos and Doctoral Consortium, xAI-2023: LB-D-DC

Lisbon, 26 July 2023 through 28 July 2023

Indexing

Scopus - 1 Citation

Other information

Authenticus ID: P-00Z-FRB

Abstract (EN): Advances in artificial intelligence trigger transformations that make more and more companies enter Industry 4.0 and 5.0 eras. In many cases, these transformations are gradual and performed in a bottom-up manner. This means that in the first step, the industrial hardware is upgraded to collect as much data as possible without actual planning of the utilization of the information. Furthermore, the data storage and processing infrastructure is prepared to keep large volumes of historical data accessible for further analysis. Only in the last step are methods for processing the data developed to improve or gain more insight into the industrial and business processes. Such a pipeline makes many companies face a problem with huge amounts of data, an incomplete understanding of how the existing knowledge is represented in the data, under which conditions the knowledge no longer holds, or what new phenomena are hidden inside the data. We argue that this gap needs to be addressed by the next generation of XAI methods which should be expert-oriented and focused on knowledge generation tasks rather than model debugging. The paper is based on the findings of the EU CHIST-ERA project on Explainable Predictive Maintenance (XPM). © 2023 CEUR-WS. All rights reserved.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 5