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——聚焦于深度神经网络（DNNs）与大型语言模型（LLMs）——并指出了当前XAI在实证与概念层面的局限性。我们探讨了源于更深层根源（即两个悖论、两种概念混淆及五项错误假设）的关键症候。当前XAI研究领域的这些根本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；在实践层面，进一步尝试改革这种悖论性的XAI可能加剧其混乱——这要求根本性的转变与新的研究方向。为突破XAI的局限，我们提出了一个四维综合范式转变，旨在迈向可靠且可认证的AI开发。这四个组成部分包括：以验证为核心的交互式人工智能（IAI），旨在建立科学共同体协议以认证AI系统性能，而非试图进行事后解释；AI认识论，以奠定严谨的科学基础；用户可感知AI，用于创建适应特定用户群体的情境感知系统；以及模型中心可解释性，以实现忠实的技术分析——这些共同构成了全面的后XAI研究方向。