33 lines
2.7 KiB
Markdown
33 lines
2.7 KiB
Markdown
## What to build
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Two supporting documentation tasks that can run in parallel with issue 0009:
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**1. Move governance reference documents to `docs/`**
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Move `docs/research/governance_principles/ai-constitution.md` and `docs/research/governance_principles/HUMANS.md` to `docs/`. These are human-facing reference documents — the full evidence base and the practitioner checklist — not agent instructions. They belong alongside VISION.md and ROADMAP.md, not in the research folder.
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Update any cross-references between these files and the remaining research files (`ai-governance-research.md`, `ai-governance-research-challenges.md`, `ai-governance-research-session.md`, `ai-agent-instructions-notes.md`) to reflect their new paths. The research files stay in `docs/research/governance_principles/` as the audit trail for the constitution.
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**2. Add governance domain language to `CONTEXT.md`**
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Add the following terms to the `CONTEXT.md` glossary so future chunks (skills, workflows, agent roles) resolve them consistently:
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- **HITL** (human-in-the-loop) — agent pauses before a consequential action; human approves before execution. Required for irreversible or high-stakes actions.
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- **HOTL** (human-on-the-loop) — agent acts; human monitors and can intervene after the fact. Acceptable for low-stakes, bounded, reversible actions.
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- **Symbolic oversight** — oversight implemented as a gesture (assigning a reviewer) rather than a functional safeguard. The documented failure mode: a reviewer without the information, time, agency, or intent to evaluate is not oversight.
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- **Data classification tiers** — the four-tier framework governing what data may enter AI context: Public (no restrictions), Internal (enterprise AI tools only), Confidential (enterprise AI with data-not-trained commitment), Restricted (never enters AI context — hard architectural prohibition).
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- **Sycophancy** — the failure mode where RLHF-trained models prioritise approval over accuracy. Treated as a first-class reliability risk: models change correct answers to wrong ones under user pressure and persist in the wrong answer. Designing against sycophancy is an explicit obligation, not a quality-of-life concern.
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## Acceptance criteria
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- [x] `docs/ai-constitution.md` exists (moved from research folder)
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- [x] `docs/HUMANS.md` exists (moved from research folder)
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- [x] Neither file remains in `docs/research/governance_principles/`
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- [x] Cross-references within the moved files point to their new paths
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- [x] `CONTEXT.md` glossary contains entries for HITL, HOTL, symbolic oversight, data classification tiers, and sycophancy
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- [x] Each glossary entry is precise and consistent with the definitions in `docs/ai-constitution.md`
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## Blocked by
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None — can start immediately.
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