This study provides a cross-disciplinary examination of Explainable Artificial Intelligence (XAI) approaches-focusing on deep neural networks (DNNs) and large language models (LLMs)-and identifies empirical and conceptual limitations in current XAI. We discuss critical symptoms that stem from deeper root causes (i.e., two paradoxes, two conceptual confusions, and five false assumptions). These fundamental problems within the current XAI research field reveal three insights: experimentally, XAI exhibits significant flaws; conceptually, it is paradoxical; and pragmatically, further attempts to reform the paradoxical XAI might exacerbate its confusion-demanding fundamental shifts and new research directions. To move beyond XAI's limitations, we propose a four-pronged synthesized paradigm shift toward reliable and certified AI development. These four components include: verification-focused Interactive AI (IAI) to establish scientific community protocols for certifying AI system performance rather than attempting post-hoc explanations, AI Epistemology for rigorous scientific foundations, User-Sensible AI to create context-aware systems tailored to specific user communities, and Model-Centered Interpretability for faithful technical analysis-together offering comprehensive post-XAI research directions.

翻译：本研究对可解释人工智能（XAI）方法进行了跨学科审视——重点关注深度神经网络（DNN）和大语言模型（LLM）——并识别了当前XAI在经验与概念层面的局限性。我们探讨了源于深层根本原因的关键症状（即两个悖论、两个概念混淆以及五个错误假设）。当前XAI研究领域的这些根本性问题揭示了三点洞见：在实验层面，XAI存在显著缺陷；在概念层面，其具有悖论性；在实用层面，进一步修正悖论性的XAI可能会加剧其混乱——需要根本性的转变与新的研究方向。为突破XAI的局限，我们提出了一个四管齐下的综合范式转变，以推动可靠且可认证的AI发展。这四个组成部分包括：以验证为中心的交互式AI（IAI），用于建立认证AI系统性能的科学共同体协议，而非尝试事后解释；AI认识论，用于建立严谨的科学基础；用户感知型AI，用于构建针对特定用户社区的上下文感知系统；以及以模型为中心的可解释性，用于实现忠实的技术分析——共同提供了全面的后XAI研究方向。