In the quest for accurate and interpretable AI models, eXplainable AI (XAI) has become crucial. Fuzzy Cognitive Maps (FCMs) stand out as an advanced XAI method because of their ability to synergistically combine and exploit both expert knowledge and data-driven insights, providing transparency and intrinsic interpretability. This letter introduces and investigates the "Total Causal Effect Calculation for FCMs" (TCEC-FCM) algorithm, an innovative approach that, for the first time, enables the efficient calculation of total causal effects among concepts in large-scale FCMs by leveraging binary search and graph traversal techniques, thereby overcoming the challenge of exhaustive causal path exploration that hinder existing methods. We evaluate the proposed method across various synthetic FCMs that demonstrate TCEC-FCM's superior performance over exhaustive methods, marking a significant advancement in causal effect analysis within FCMs, thus broadening their usability for modern complex XAI applications.

翻译：在追求准确且可解释的AI模型过程中，可解释人工智能（XAI）变得至关重要。模糊认知图（FCMs）作为一种先进的XAI方法，因其能够协同结合并利用专家知识与数据驱动的见解，提供透明性和内在可解释性而脱颖而出。本文介绍并研究了“FCM总因果效应计算”（TCEC-FCM）算法，这是一种创新方法，首次通过利用二分查找和图遍历技术，实现了大规模FCM中概念间总因果效应的高效计算，从而克服了现有方法中因穷举因果路径探索而面临的挑战。我们在多种合成FCM上评估了所提方法，结果表明TCEC-FCM在性能上优于穷举方法，标志着FCM因果效应分析领域的重要进展，从而拓宽了其在现代复杂XAI应用中的可用性。