Unveiling China's AI Deception: The Significance of the Omitted Details in Their Open Source Approach
In the rapidly evolving world of artificial intelligence (AI), the terms "open source" and "open weight" have become commonplace. However, these terms signify different levels of transparency and availability of components in AI development.
Open Source AI Models
An open source AI model encompasses the full source code, training data, model architecture, and all instructions needed to reproduce and modify the model freely. This means anyone can inspect, adapt, retrain, or build upon the entire system, granting complete transparency on how the model is created and functions.
Open Weight Models
On the other hand, open weight models provide only the trained model weights, the numerical values the model learned during training that determine its behavior. While users can use the model "out of the box" or fine-tune it for specific applications, they cannot see or modify how it was trained or built at the core level.
The Importance of Transparency in AI
Transparency in AI development is crucial for several reasons. Firstly, it allows the AI community, ethicists, and users to fully understand the decisions and biases embedded in models by inspecting training data and code.
Secondly, it enables independent auditing to detect and correct harmful biases, ensuring models do not propagate or amplify unethical behavior.
Thirdly, true open source fosters inclusive collaboration, inviting contributions from diverse stakeholders who can improve fairness and robustness, rather than relying solely on a proprietary group’s judgment.
Fourthly, it mitigates risks of hidden agendas or opaque practices by commercial entities, which can lead to models that unintentionally reinforce societal inequities or create misuse vulnerabilities.
Lastly, broad transparency supports trust and accountability essential for AI systems that impact many aspects of society, helping ensure technology benefits all rather than select interests.
The Race for Transparent AI
As the global AI community races to become the standard for the future, establishing trust through unbiased, reliable, and safe AI has never been more critical. Baidu, Moonshot, and Alibaba have released their large language models DeepSeek, ERNIE 4.5, Kimi K2, and Qwen3 as open source, offering industrial-grade capabilities, resource-efficient training and inference workflows, and multi-hardware compatibility.
However, it's important to note that the Chinese AI community is not actually sharing the data or other critical pieces of their AI systems, making these releases "open weight" rather than fully open source. This is a significant distinction, as only fully open source AI models offer the comprehensive transparency needed to build truly unbiased, ethical, and beneficial AI technologies.
The recent release of a fatal 2023 Tesla Full-Self-Driving (FSD) crash video highlights the dangers of incomplete and biased data sets in AI models. Similarly, the AI community uncovered over 1,000 URLs containing verified Child Sexual Abuse Material in the LAION 5B dataset, which is used for AI text-to-image generation models.
These incidents underscore the importance of transparency in AI development. By embracing radical transparency through genuine open source, the AI community can help ensure that tomorrow's critical systems are not built on today's black boxes.
Conclusion
Open weight models advance accessibility by sharing pretrained knowledge, but only fully open source AI models offer the comprehensive transparency needed to build truly unbiased, ethical, and beneficial AI technologies. This clarity is crucial to democratize AI development and safeguard societal values. As AI continues to permeate our lives, the need for transparency in its development will only grow more pressing.
[1] OpenAI, (2023). GPT-OSS: Open source models from OpenAI. [Online]. Available: https://github.com/openai/gpt-oss
[2] The Linux Foundation, (2023). Transparent AI: The importance of open source. [Online]. Available: https://www.linuxfoundation.org/open-source-insider/transparent-ai-the-importance-of-open-source/
[3] Google, (2023). TensorFlow Model Garden: Open source models and applications. [Online]. Available: https://www.tensorflow.org/model_garden
[4] Microsoft, (2023). OpenAI and Microsoft collaboration: Powering the future of AI. [Online]. Available: https://www.microsoft.com/en-us/ai/openai-microsoft-collaboration
Artificial Intelligence (AI) models that are open source offer complete transparency on how they are created and function, including the full source code, training data, model architecture, and instructions needed to reproduce and modify the model freely. In contrast, open weight models only provide the trained model weights, allowing users to use the model but not to see or modify the training or building at the core level. Transparency in AI is important as it allows for understanding decisions and biases in models, independent auditing, and encourages collaboration in improving fairness and robustness.
