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An Improved Summary–Explanation Method for Promoting Trust Through Greater Support with Application to Credit Evaluation Systems

Author

Listed:
  • Chen Peng

    (College of Computer Science and Engineering, Jishou University, Jishou 416000, China)

  • Tianci He

    (College of Communication and Electronic Engineering, Jishou University, Jishou 416000, China)

Abstract

Decision support systems are being increasingly applied in critical decision-making domains such as healthcare and criminal justice. Trust in these systems requires transparency and explainability. Among the forms of explanation, globally consistent summary–explanation (SE) is a rule-based local explanation offering useful global information and 100% dataset consistency. However, globally consistent SEs with limited complexity often have a small amount of support, making them unconvincing. To improve the support of SEs, this paper introduces the q-consistent SE, trading slightly lower consistency for greater support. The challenge is solving the maximizing support with the q-consistency (MSqC) problem, which is more complex than maximizing support for global consistency, leading to extended solution times using standard solvers. To enhance efficiency, the paper proposes a weighted column sampling (WCS) method, using simplified increase support (SIS) scores to create and solve smaller problem instances. Experiments on credit evaluation scenarios confirm that the SIS-based WCS method on MSqC problems improves scalability and yields SEs with greater support and better global extrapolation effectiveness.

Suggested Citation

  • Chen Peng & Tianci He, 2025. "An Improved Summary–Explanation Method for Promoting Trust Through Greater Support with Application to Credit Evaluation Systems," Mathematics, MDPI, vol. 13(8), pages 1-22, April.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:8:p:1305-:d:1636053
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