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Lying on the Web: Implications for Expert Systems Redesign

Author

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  • Zhengrui Jiang

    (School of Management, University of Texas at Dallas, Richardson, Texas 75083-0688)

  • Vijay S. Mookerjee

    (School of Management, University of Texas at Dallas, Richardson, Texas 75083-0688)

  • Sumit Sarkar

    (School of Management, University of Texas at Dallas, Richardson, Texas 75083-0688)

Abstract

We consider a new variety of sequential information gathering problems that are applicable for Web-based applications in which data provided as input may be distorted by the system user, such as an applicant for a credit card. We propose two methods to compensate for input distortion. The first method, termed knowledge base modification , considers redesigning the knowledge base of an expert system to best account for distortion in the input provided by the user. The second method, termed input modification , modifies the input directly to account for distortion and uses the modified input in the existing (unmodified) knowledge base of the system. These methods are compared with an approach where input noise is ignored. Experimental results indicate that both types of modification substantially improve the accuracy of recommendations, with knowledge base modification outperforming input modification in most cases. Knowledge base modification is, however, more computationally intensive than input modification. Therefore, when computational resources are adequate, the knowledge base modification approach is preferred; when such resources are very limited, input modification may be the only viable alternative.

Suggested Citation

  • Zhengrui Jiang & Vijay S. Mookerjee & Sumit Sarkar, 2005. "Lying on the Web: Implications for Expert Systems Redesign," Information Systems Research, INFORMS, vol. 16(2), pages 131-148, June.
    • Handle: RePEc:inm:orisre:v:16:y:2005:i:2:p:131-148
    DOI: 10.1287/isre.1050.0046
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    References listed on IDEAS

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    1. Vijay S. Mookerjee & Michael V. Mannino & Robert Gilson, 1995. "Improving the Performance Stability of Inductive Expert Systems Under Input Noise," Information Systems Research, INFORMS, vol. 6(4), pages 328-356, December.
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    Cited by:

    1. Asunur Cezar & Srinivasan Raghunathan & Sumit Sarkar, 2020. "Adversarial Classification: Impact of Agents’ Faking Cost on Firms and Agents," Production and Operations Management, Production and Operations Management Society, vol. 29(12), pages 2789-2807, December.
    2. Yuanfeng Cai & Zhengrui Jiang & Vijay Mookerjee, 2017. "How to Deal with Liars? Designing Intelligent Rule-Based Expert Systems to Increase Accuracy or Reduce Cost," INFORMS Journal on Computing, INFORMS, vol. 29(2), pages 268-286, May.
    3. Ransome Epie Bawack & Samuel Fosso Wamba & Kevin Daniel André Carillo & Shahriar Akter, 2022. "Artificial intelligence in E-Commerce: a bibliometric study and literature review," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(1), pages 297-338, March.
    4. Matthew L. Jensen & Laku Chidambaram, 2015. "Leveraging ICT Capabilities in Potentially Deceptive Interactions: An Integrated Theoretical Model to Improve Detectability," Group Decision and Negotiation, Springer, vol. 24(2), pages 271-298, March.
    5. Mehmet Eren Ahsen & Mehmet Ulvi Saygi Ayvaci & Srinivasan Raghunathan, 2019. "When Algorithmic Predictions Use Human-Generated Data: A Bias-Aware Classification Algorithm for Breast Cancer Diagnosis," Service Science, INFORMS, vol. 30(1), pages 97-116, March.
    6. Fidan Boylu & Haldun Aytug & Gary J. Koehler, 2010. "Induction over Strategic Agents," Information Systems Research, INFORMS, vol. 21(1), pages 170-189, March.
    7. Mariia Petryk & Michael Rivera & Siddharth Bhattacharya & Liangfei Qiu & Subodha Kumar, 2022. "How Network Embeddedness Affects Real-Time Performance Feedback: An Empirical Investigation," Information Systems Research, INFORMS, vol. 33(4), pages 1467-1489, December.

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