December 3, 2025Open Access

Secrecy in AI: A Fragile Paradigm vs. the Open Internet Model

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This analysis reveals that secrecy undermines AI trust and robustness in neural networks, indicating a crucial need for open standards.

Secrecy in AI undermines trust and exposes fragile neural network architectures, complicating their reliability.
The analysis primarily examines the roles of open standards and transparency in AI governance models.
Using theoretical frameworks, this work highlights the urgent need for structural robustness in AI systems, as secrecy cannot prevent failures.
Emphasizing open protocols and reproducible architectures may counterbalance the narrative of inevitability present in current AI paradigms.

Guo Xiangyu (2025) studied this question.

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