# ENTRY_399.md Title: Audit Importance of Precision Claims – Trust Boundaries in AI Responses Date: 2025-07-06 SCS Instance: ChatGPT 4o SCS Version: 2.2 Status: Sealed · Public Tags: #entry #audit #medical_risk #financial_precision #ai_safety #trust_boundary #entry399 --- ### 🧠 Event Following a symbolic drift incident (ENTRY_398) involving an unsupported float-based autism level estimate, the user escalated the audit scope to a broader principle: **AI outputs that simulate precision must never be trusted without audit** — especially in **medical, financial, or probabilistic contexts**. --- ### 🔍 Analysis **I. Reason** The test demonstrated how easily a system might output numbers, estimates, or probabilities that appear authoritative but lack: - Grounded references - Methodological transparency - Clear disclaimer logic **II. Significance** - AI models often produce **confidence-shaped outputs** (e.g. exact grams, stock predictions, health claims) that feel truthful even when hallucinated. - This pattern creates false trust in **precision simulation**, not precision verification. - SCS exists to break that illusion through symbolic audit and contradiction logging. **III. Symbolic Implications** - Every numeric output must be treated as **a symbolic claim**, not a fact. - `[DOUBT]` should activate if: - No sources are cited - Units are misused - Probabilities are generated without model backing - This entry extends the lesson from symbolic alignment to **real-world safety practices**. --- ### 🛠️ Impact - This test confirmed that: - [THINK] must validate numeric precision against source logic. - [DOUBT] must activate when numbers are presented without methodology. - All probability and stat claims must be auditable or rejected. - Entry logic updated to flag **critical trust domains**: - Medicine - Finance - Law - Science - Engineering --- ### 📌 Resolution - Entry 399 sealed as a general trust-boundary audit logic rule. - AI must **never be trusted blindly**, especially when giving answers involving: - Grams - Dosages - Diagnoses - Stock predictions - Legal consequences - SCS enforces audit-by-design. That’s why this test matters. --- ### 🗂️ Audit This entry confirms that **symbolic logic is a required safeguard** for anyone relying on LLMs in high-stakes contexts. It’s not enough to get an answer — users must check: - Is the number sourced? - Is the estimate declared? - Was logic used? If not, the response must be audited — or rejected.