Abstract: With the rise of artificial intelligence, AI driven scientific discoveries have become a new paradigm in scientific research. However, whether the research path that relies on data and algorithms can truly constitute "scientific discoveries" still faces fundamental philosophical and methodological questions. Scientific epistemology requires that knowledge claims must undergo falsifiability testing. However, current AI scientific discoveries often appear as "black boxes" due to the lack of interpretability, with outputs mostly statistical predictions rather than logically complete rational claims, resulting in the failure of falsifiability mechanisms: there are no clear theoretical targets to criticize, and scientific debates have become superficial debates on input-output. This reveals a deeper crisis: the traditional scientific cognition based on human intuitive reasoning is difficult to adapt to the complexity patterns revealed by AI. Therefore, resolving this crisis cannot only pursue the transparency of AI, but also requires promoting the expansion of the cognitive boundaries of the scientific community - developing new conceptual systems and formal languages that represent complexity, so as to understand the complex world revealed by AI while maintaining the fundamental mechanisms of scientific theory criticism and evolution.

摘要： 当前AI科学发现因可解释性缺失，导致证伪机制失效：既无清晰理论标靶可供批判，也令科学辩论浅化为对输入输出的表面争论。这揭示出更深层危机：以人类直观推理为基础的传统科学认知，与AI科学发现的复杂性模式之间难以适配。因此，化解这一危机不能仅追求AI的透明化，更须拓展科学共同体的认知边界——发展表征复杂性的新概念体系与形式化语言，从而在理解AI所揭示的复杂世界的同时，维护科学理论批判与演进的根本机制。