Research Progress of Interpretable Artificial Intelligence

doi:10.19678/j.issn.1000-3428.0069925

Abstract

Abstract:

Artificial intelligence has made remarkable progress across many fields, encouraging countries to attach great importance to its research and development. However, the rapid development of artificial intelligence has also brought about a series of problems and threats, and overreliance on and blind trust in such models can lead to serious risks. Therefore, interpretable artificial intelligence has become a key element in building trusted and transparent intelligent systems, and its research and development requires immediate attention. This survey comprehensively summarizes the research progress on explainable artificial intelligence at home and abroad comprehensively from multiple dimensions and levels. Based on current research results in the industry, this survey subdivides the key technologies of explainable artificial intelligence into four categories: interpretation model, interpretation method, safety testing, and experimental verification, with the aim of clarifying the technical focus and development direction of each field. Furthermore, the survey explores specific application examples of explainable artificial intelligence across key industry sectors, including but not limited to education, healthcare, finance, autonomous driving, and justice, demonstrating the significant role it plays in enhancing decision-making transparency. Finally, this survey provides an in-depth analysis of the major technical challenges of interpretable artificial intelligence and presents future development trends, in addition to a special investigation and in-depth analysis of the interpretability of large models, which has attracted considerable attention recently.

Key words: interpretability, trustworthy, artificial intelligence, demonstration application, large model

摘要：

人工智能在诸多领域的应用取得了突破性的进展, 引起了全球各国对其研发的高度重视。然而, 人工智能的快速发展也带来了一系列的问题, 过度依赖和盲目信任人工智能模型可能导致严重的风险。因此, 可解释人工智能成为构建可信、透明的智能系统的关键要素, 其研发变得尤为迫切。为此, 本文综述可解释人工智能的国内外研究进展, 从多维度、多层次进行全面梳理与归纳。首先, 基于当前行业内的研究成果, 将可解释人工智能的关键技术细分为解释模型、解释方法、安全测试及实验验证4类, 旨在明确各领域的技术焦点与发展方向。然后, 探讨可解释人工智能在多个关键行业领域的具体应用实例, 包括但不限于教育、医疗、金融、自动驾驶及司法等, 展示其在提升决策透明度等方面的重要作用。最后, 深入剖析可解释人工智能当前面临的主要技术挑战, 并展望其未来的发展趋势, 尤其针对当前备受瞩目的大模型可解释性问题, 进行了专项调研与探讨分析。

关键词: 可解释性, 可信, 人工智能, 示范应用, 大模型

LIAO Yong, HAN Xiaojin, LIU Jinlin, WANG Hao. Research Progress of Interpretable Artificial Intelligence[J]. Computer Engineering, 2026, 52(3): 41-61.

廖勇, 韩小金, 刘金林, 汪浩. 可解释人工智能研究进展[J]. 计算机工程, 2026, 52(3): 41-61.

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Figures/Tables 12

Fig.1 Classification of key technologies for interpretable artificial intelligence

Fig.2 Schematic diagram of cross-modal knowledge distillation

Fig.3 Classification of interpretable artificial intelligence by interpretation methods

Fig.4 Structure of CAM overall network

Fig.5 Structure of LDP-based privacy protection model

Fig.6 Structure of the atomic method for service-based trusted evidence collection

Fig.7 Schematic diagram of FRCNN-based diagnosis method

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