AI-Enhanced Hybrid Decision Management

Dominik Bork (), Syed Juned Ali and Georgi Milenov Dinev
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Dominik Bork: TU Wien
Syed Juned Ali: TU Wien
Georgi Milenov Dinev: TU Wien

Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, 2023, vol. 65, issue 2, No 6, 179-199

Abstract: Abstract The Decision Model and Notation (DMN) modeling language allows the precise specification of business decisions and business rules. DMN is readily understandable by business users involved in decision management. However, as the models get complex, the cognitive abilities of humans threaten manual maintainability and comprehensibility. Proper design of the decision logic thus requires comprehensive automated analysis of e.g., all possible cases the decision shall cover; correlations between inputs and outputs; and the importance of inputs for deriving the output. In the paper, the authors explore the mutual benefits of combining human-driven DMN decision modeling with the computational power of Artificial Intelligence for DMN model analysis and improved comprehension. The authors propose a model-driven approach that uses DMN models to generate Machine Learning (ML) training data and show, how the trained ML models can inform human decision modelers by means of superimposing the feature importance within the original DMN models. An evaluation with multiple real DMN models from an insurance company evaluates the feasibility and the utility of the approach.

Keywords: Enterprise modeling; DMN; Model-driven engineering; Artificial intelligence; Machine learning; Explainable AI (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s12599-023-00790-2

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