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3392. Reducing Cognitive Load in Declarative Business Process Models
Invited abstract in session MC-27: XAI in Business Processes, stream Mathematical Optimization for XAI.
Monday, 12:30-14:00Room: 047 (building: 208)
Authors (first author is the speaker)
| 1. | Hugo A. López
|
| DTU Compute, Technical University of Denmark |
Abstract
Explainability in Artificial Intelligence (AI) is pivotal for enabling humans to comprehend both the outputs produced by algorithms and the underlying reasoning processes. Extending it to the realm of Business Process Management (BPM), it encompasses understanding the models generated by process discovery algorithms and deciphering the recommendations offered by process engines during process enactment.
Declarative Business Process Modelling (DBPM) emerges as a crucial framework within BPM, focusing on elucidating cause-effect relations between activities. This type of explanation is particularly valuable in knowledge-intensive processes with humans in the loop.
In this talk, I summarize our recent endeavors aimed at alleviating the cognitive load associated with understanding declarative business process models. Our approach draws insights from process modeling, event-based systems, human-computer interaction, and game design.
Specifically, we will explore techniques such as enhancing traceability, ensuring semantic transparency, leveraging explain-by-example methodologies, and employing design-via-metaphors. These strategies are designed to refine the visual representations of declarative process notations, thus enhancing the accessibility and comprehensibility of knowledge-intensive processes for novice users.
Keywords
- Modeling Systems and Languages
- Decision Support Systems
- Knowledge Engineering and Management
Status: accepted
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