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AI-Enhanced Hybrid Decision Management

Author

Listed:
  • Dominik Bork

    (TU Wien)

  • Syed Juned Ali

    (TU Wien)

  • Georgi Milenov Dinev

    (TU Wien)

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.

Suggested Citation

  • Dominik Bork & Syed Juned Ali & Georgi Milenov Dinev, 2023. "AI-Enhanced Hybrid Decision Management," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 65(2), pages 179-199, April.
    • Handle: RePEc:spr:binfse:v:65:y:2023:i:2:d:10.1007_s12599-023-00790-2
    DOI: 10.1007/s12599-023-00790-2
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    References listed on IDEAS

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    1. Douglas Heaven, 2019. "Why deep-learning AIs are so easy to fool," Nature, Nature, vol. 574(7777), pages 163-166, October.
    2. Ning Xu & Jiangping Wang & Guojun Qi & Thomas Huang & Weiyao Lin, 2015. "Ontological Random Forests for Image Classification," International Journal of Information Retrieval Research (IJIRR), IGI Global, vol. 5(3), pages 61-74, July.
    3. Kurt Sandkuhl & Hans-Georg Fill & Stijn Hoppenbrouwers & John Krogstie & Florian Matthes & Andreas Opdahl & Gerhard Schwabe & Ömer Uludag & Robert Winter, 2018. "From Expert Discipline to Common Practice: A Vision and Research Agenda for Extending the Reach of Enterprise Modeling," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 60(1), pages 69-80, February.
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