🤖 AI Summary
TOPSIS suffers from poor interpretability due to its black-box nature—particularly under high-dimensional criteria and heterogeneous criterion weights. Existing multidimensional sensitivity diagram (MSD) visualization tools assume equal criterion weights and lack the capability to model weight sensitivity. This paper pioneers the integration of explainable AI (XAI) principles into classical multi-criteria decision-making (MCDM), proposing a weight–ranking response surface visualization framework. Leveraging gradient-based sensitivity analysis and local linear approximation, combined with systematic parameter-space sampling, the framework enables interactive counterfactual analysis and robustness assessment within a D3.js implementation. Evaluated across multiple benchmark datasets, the method precisely identifies critical weight-perturbation thresholds that trigger rank transitions, thereby substantially enhancing decision-makers’ understanding of and trust in TOPSIS’s internal aggregation logic.