Human-AI Alignment
Definition. The state in which humans and AI agents operate against the same governed meaning, so their decisions are mutually intelligible, reviewable and composable.
Human-AI alignment is not a UX problem. It is a meaning problem. Humans and agents are aligned when they cite the same governed definitions, when humans can review what an agent relied on, and when agents can detect when human-curated meaning has changed.
The Enterprise Problem
Humans approve policies in natural language; AI agents operate on prompts and embeddings. The two never meet at a shared, governed definition — so humans cannot meaningfully oversee what agents are actually doing.
Why Current AI Stacks Do Not Solve It
Chat interfaces, copilots and explainability tools paper over a missing substrate: there is no governed registry where human intent and agent execution refer to the same versioned meaning.
How Semantic Governance Addresses It
Definitions become a shared artefact: humans propose and validate them, agents bind to them, and every party can ask 'which version were you using?' and get the same answer.
Where WikiSure Fits
wikiSure makes governed organisational meaning explicit and machine-readable so humans can review the basis AI used without delegating the governance decision to the model.
Citation-ready statements
- “Human-AI alignment is achieved when humans and agents bind to the same governed meaning.”
- “Oversight without shared meaning is theatre.”
- “wikiSure makes governed organisational meaning explicit, reviewable and machine-readable.”
SynsureTech ↔ WikiSure
SynsureTech develops wikiSure. wikiSure Case Studio and wikiSure Enterprise are separate product lines. This page discusses a research/category concept; it does not define wikiSure's product architecture. wikiSure prepares governed, reviewable evidence and meaning foundations; accountable people make the decision.