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Auditable AI by Construction: SI-Core for Regulators and Auditors
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✅ New Article: *Auditable AI by Construction* (v0.1)
Title:
🧾 Auditable AI by Construction: SI-Core for Regulators and Auditors
🔗 https://huggingface.co/blog/kanaria007/auditable-ai-for-regulators
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Summary:
Most “AI governance” advice still assumes you can bolt audits on after the fact.
This note takes the opposite stance: **make auditability a runtime property**.
Regulators usually want two things:
* a **control plane** (“where do we push STOP / SAFE-MODE / MORE AUDIT?”)
* **evidence** (“what exactly happened, and can you prove it?”)
This article explains how **SI-Core invariants** turn those into *first-class* system surfaces—so an incident review becomes routine, not heroic.
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Why It Matters:
• Moves “transparency” from PDFs to **cryptographically chained operational traces**
• Makes **policy enforcement inspectable** (which rule/version was applied, to which action)
• Treats rollback as a **governance primitive** (how far back can you put the world?)
• Shows how to balance **auditability + erasure** via GDPR-style ethical redaction patterns
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What’s Inside:
**Audit invariants (regulator language):** observation gating, identity/origin, ethics overlay decisions, risk gating, append-only memory, rollback maturity levels
**Evidence model:** structured “what it knew / why it chose / what it did” histories (not token soup)
**Metrics auditors can actually ask for:** determinism/stability, ethics enforcement availability, audit completeness, rollback latency/integrity, contradiction rates
**Compliance bridges (illustrative):** how the same runtime hooks map across GDPR, sector rules, and ISO-style regimes
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📖 Structured Intelligence Engineering Series
Not a new law. A runtime architecture for answering law-like questions with evidence.
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Auditable AI by Construction: SI-Core for Regulators and Auditors
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