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：本文对可解释人工智能（XAI）方法进行了跨学科的审视——重点关注深度神经网络（DNNs）和大语言模型（LLMs）——并识别出当前XAI在实证和概念上的局限性。我们讨论了源于深层根源的关键症状（即两个悖论、两个概念混淆和五个错误假设）。当前XAI研究领域内的这些根本问题揭示了三点洞见：实验上，XAI存在显著缺陷；概念上，它自相矛盾；实用上，进一步试图改革悖论性的XAI可能加剧其混乱——这要求根本性的转变和新的研究方向。为超越XAI的局限性，我们提出一个四管齐下的综合范式转变，迈向可靠且经认证的AI开发。这四个组成部分包括：面向验证的交互式AI（IAI），旨在建立用于认证AI系统性能的科学社区协议，而非尝试事后解释；AI认识论，以建立严谨的科学基础；用户感知型AI，创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，用于进行忠实的技术分析——共同提供全面的后XAI研究方向。