Raw LLM Responses

Inspect the exact model output for any coded comment.

Comment
The “Ministries shift from operators to supervisors” line is the real story here. Once agents start executing licensing, compliance, approvals, and public services, the hard layer becomes agent governance: permissions, audit trails, rollback, exception handling, and who signs off when the agent is technically correct but contextually risky. That’s the difference between automation at scale and a public operating system people can actually trust.
LinkedIn AI Policy & Regulation Enterprise AI Adoption in Saudi Arabia | Sovere… 2026-05-25T09:5…
Coding Result
DimensionValue
Primary valueaccountability
Secondary valuetransparency
Alignment targetsociety
Stancedemanding
Emotionapproval
Value justificationThe speaker emphasizes the need for agent governance, including permissions, audit trails, and rollback, which implies a desire for accountability in AI systems.
Target justificationThe comment discusses the impact of AI on government operations and public services, indicating that the target of alignment is the broader society.
Coded at2026-06-11T08:06:09Z
Raw LLM Response
```json { "value_primary": "accountability", "value_secondary": "transparency", "target": "society", "stance": "demanding", "emotion": "approval", "value_justification": "The speaker emphasizes the need for agent governance, including permissions, audit trails, and rollback, which implies a desire for accountability in AI systems.", "target_justification": "The comment discusses the impact of AI on government operations and public services, indicating that the target of alignment is the broader society." } ```