Model Value Embedding / 模型价值观嵌入

Model value embedding is the claim that a large model’s outputs reflect the data, rewards, post-training choices, product policies, and institutional contexts that shaped it. In 174. 我们还能给算法当多久的品味老师？｜对谈亚马逊AGI查晟, 查晟 / Cha Sheng argues that language models are built from human symbols rather than direct access to reality, so their apparent neutrality is a product surface over selected data and reward signals.

The concept complements AI Model Value Surveying. Surveying measures output tendencies after the fact; value embedding asks how those tendencies enter the model through training and governance. The episode uses Anthropic and Claude as a public example where personality, constitution, and value choices are explicitly discussed rather than hidden.

Key Claims

• Model values can enter through pretraining data, filtering, reward design, post-training, product policy, and deployment context.
• Neutrality should be treated as an interface claim, not as proof that no values were selected.
• Value embedding matters more when models advise users, represent companies, or become national information infrastructure.
• Enterprise and national model ownership are partly value-control strategies, not only cost or accuracy strategies.
• Alignment governance has to govern the people and institutions shaping model values, not only the final output behavior.

Connections

• AI Model Value Surveying - output-side measurement of model value tendencies.
• AI Advice Moral Outsourcing, AI Model Censorship, and Language-Dependent AI Bias - downstream problems when model defaults enter advice or public information.
• AI Alignment Governance - institutional governance frame for value selection.
• Enterprise Owned Models and Sovereign AI Models / 主权AI模型 - ownership routes for controlling data, value expression, and representation.
• Anthropic and Claude - example model provider and model named in the source.