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The Use and Effects of Knowledge-Based System Explanations: Theoretical Foundations and a Framework for Empirical Evaluation

Author

Listed:
  • Jasbir S. Dhaliwal

    (Department of Decision Sciences, Faculty of Business Administration, National University of Singapore, Kent Ridge, Singapore 0511)

  • Izak Benbasat

    (Management Information Systems Division, Faculty of Commerce and Business Administration, University of British Columbia, 2053, Main Mall, Vancouver, British Columbia, Canada V6T 1Z2)

Abstract

Ever since MYCIN introduced the idea of computer-based explanations to the artificial intelligence community, it has come to be taken for granted that all knowledge-based systems (KBS) need to provide explanations. While this widely-held belief has led to much research on the generation and implementation of various kinds of explanations, there has been no theoretical basis to justify the use of explanations by KBS users. This paper discusses the role of KBS explanations to provide an understanding of both the specific factors that influence explanation use and the consequences of such use.The first part of the paper proposes a model based on cognitive learning theories to identify the reasons for the provision of KBS explanations from the perspective of facilitating user learning. Using the feedforward and feedback operators of cognitive learning the paper develops strategies for providing KBS explanations and classifies the various types of explanations found in current KBS applications.This second part of the paper presents a two-part framework to investigate empirically the use of KBS explanations. The first part of the framework focuses on the potential factors that influence the explanation seeking behavior of KBS users, including user expertise, the types of explanations provided and the level of user agreement with the KBS. The second part of the framework explores the potential effects of the use of KBS explanations and specifically considers four distinct categories of potential effects: explanation use behavior, learning, perceptions, and judgmental decision making.

Suggested Citation

  • Jasbir S. Dhaliwal & Izak Benbasat, 1996. "The Use and Effects of Knowledge-Based System Explanations: Theoretical Foundations and a Framework for Empirical Evaluation," Information Systems Research, INFORMS, vol. 7(3), pages 342-362, September.
    • Handle: RePEc:inm:orisre:v:7:y:1996:i:3:p:342-362
    DOI: 10.1287/isre.7.3.342
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    Citations

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    Cited by:

    1. Jörg Mayer & Robert Winter & Thomas Mohr, 2012. "Situational Management Support Systems," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 4(6), pages 331-345, December.
    2. Moez Limayem & Gerardine DeSanctis, 2000. "Providing Decisional Guidance for Multicriteria Decision Making in Groups," Information Systems Research, INFORMS, vol. 11(4), pages 386-401, December.
    3. Geldermann, Jutta & Bertsch, Valentin & Treitz, Martin & French, Simon & Papamichail, Konstantinia N. & Hämäläinen, Raimo P., 2009. "Multi-criteria decision support and evaluation of strategies for nuclear remediation management," Omega, Elsevier, vol. 37(1), pages 238-251, February.
    4. Wulf, David & Bertsch, Valentin, 2016. "A natural language generation approach to support understanding and traceability of multi-dimensional preferential sensitivity analysis in multi-criteria decision making," MPRA Paper 75025, University Library of Munich, Germany.
    5. Montazemi, A. R. & Gupta, K. M., 1997. "On the effectiveness of cognitive feedback from an interface agent," Omega, Elsevier, vol. 25(6), pages 643-658, December.
    6. Vicky Arnold & Nicole Clark & Philip A. Collier & Stewart A. Leech & Steve G. Sutton, 2004. "Explanation provision and use in an intelligent decision aid," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 12(1), pages 5-27, January.
    7. Kevin Bauer & Moritz von Zahn & Oliver Hinz, 2023. "Expl(AI)ned: The Impact of Explainable Artificial Intelligence on Users’ Information Processing," Information Systems Research, INFORMS, vol. 34(4), pages 1582-1602, December.
    8. Khim Ong Kelly, 2007. "Feedback and Incentives on Nonfinancial Value Drivers: Effects on Managerial Decision Making," Contemporary Accounting Research, John Wiley & Sons, vol. 24(2), pages 523-556, June.
    9. Christiane B. Haubitz & Cedric A. Lehmann & Andreas Fügener & Ulrich W. Thonemann, 2021. "The Risk of Algorithm Transparency: How Algorithm Complexity Drives the Effects on Use of Advice," ECONtribute Discussion Papers Series 078, University of Bonn and University of Cologne, Germany.
    10. Bauer, Kevin & von Zahn, Moritz & Hinz, Oliver, 2022. "Expl(AI)ned: The impact of explainable Artificial Intelligence on cognitive processes," SAFE Working Paper Series 315, Leibniz Institute for Financial Research SAFE, revised 2022.

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