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Enhancing Industrial AI Usability Through Human-AI Interaction

Authors:

Marcus Hammarström , Liam Burberry Gahm , Mobyen Uddin Ahmed, Shaibal Barua, Shahina Begum, Emmanuel Weiten , Daniel Aurel

Research group:

Artificial Intelligence and Intelligent Systems

Publication Type:

Conference/Workshop Paper

Venue:

28th International Conference on Computer and Information Technology

Abstract

With the growing integration of Artificial Intelligence (AI) in industrial environments, computer vision systems offer valuable support for automating visual inspections and decision-making. This paper presents the process of integrating a trained AI model, e.g. Mask R-CNN, for excavator sprocket tooth detection and segmentation with a Human-AI Interaction (HAII)-focused user interface. Here, HAII design considers the features to include Human-AI Collaboration (HAC) into the ML life cycle, as the model operations are the only life cycle stage that is of interest and implemented in the interface. The system was evaluated for user acceptance and ML performance. Results show that HAII integration enhances usability and user engagement—particularly through guided image capture and quality assessment—although it did not significantly improve model performance.

Bibtex

@inproceedings{Hammarstrom7315,
author = {Marcus Hammarstr{\"o}m and Liam Burberry Gahm and Mobyen Uddin Ahmed and Shaibal Barua and Shahina Begum and Emmanuel Weiten and Daniel Aurel},
title = {Enhancing Industrial AI Usability Through Human-AI Interaction},
month = {July},
year = {2026},
booktitle = {28th International Conference on Computer and Information Technology },
url = {http://www.es.mdu.se/publications/7315-}
}