Explainable Artificial Intelligence for Process Mining: A General Overview and Application of a Novel Local Explanation Approach for Predictive Process Monitoring
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“approaches for users to interact with financial organisations [75].”
BackgroundEstablishing and evaluating trustworthy AI: overview and research challenges
Kowald, D., Scher, S., Pammer-Schindler, V., Müllner, P., Waxnegger, K., Demelius, L., Fessl, A., Toller, M., Mendoza Estrada, I. G., Šimić, I., et al. (2024) · Frontiers in Big Data · doi:10.3389/fdata.2024.1467222 · Cited by 59 · see citing works
- Authors
- Mehdiyev, N., & Fettke, P.
- Affiliations
- German Research Centre for Artificial Intelligence (DE)
- Corresponding author
- Mehdiyev, Nijat
- Venue
- Studies in Computational Intelligence
- Publisher
- Springer International Publishing
- Published
- 2021
- Pages
- 1-28
- ISSN
- 1860-949X (ISSN-L)
- 1860-9503
- OpenAlex
- W3083527131
- Cited by
- 78
- References
- 106
- Citations by year
- 2026: 5 · 2025: 20 · 2024: 18 · 2023: 17 · 2022: 13 · 2021: 5
- FWCI
- 22.22
- Type
- book chapter
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Studies in computational intelligence (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Social Sciences › Business, Management and Accounting › Management Information Systems › Business Process Modeling and Analysis
- Physical Sciences › Computer Science › Software › Software Testing and Debugging Techniques
- Keywords
- explainable AI, process mining, predictive process monitoring, local explanations, post-hoc explanation, deep learning classifier, latent representations, incident management, process-aware information systems, perturbation-based explanation
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