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

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.