Editing Openai/69509256-a990-8004-9e91-1e2cfc7e0dc4 (section)

===== Guardrails that actually block AI from “defining law” =====

These are designable as institutional rules, procurement requirements, and statutory constraints.

1) Explicit non-delegation rule (hard boundary)
* “No law, regulation, or binding guidance may be determined by an automated system; AI outputs are advisory only.”
* Pair with: named human authorship and signatory accountability for every normative clause.

2) Mandatory human oversight with competence and power
* Require that humans can understand, monitor, and override outputs and that oversight is designed to reduce rights/safety risks—this is the core logic behind “human oversight” requirements in major AI governance regimes. Artificial Intelligence Act<ref>{{cite web|title=Artificial Intelligence Act|url=https://artificialintelligenceact.eu/article/14/|publisher=Artificial Intelligence Act|access-date=2025-12-28}}</ref>

3) Traceability and provenance for every claim
* Any AI-assisted draft must include: - source list, - quotes/anchors for legal authorities, - uncertainty flags, - and a change log. NIST’s AI RMF emphasizes governance and risk management across the lifecycle, which operationally includes documentation and accountability mechanisms. NIST Publications<ref>{{cite web|title=NIST Publications|url=https://nvlpubs.nist.gov/nistpubs/ai/nist.ai.100-1.pdf|publisher=NIST Publications|access-date=2025-12-28}}</ref>

4) “No-citation, no-law” drafting rule
* If the AI proposes a legal rule, it must be tied to: - constitutional text, statute, precedent, or - clearly labeled policy objectives (not disguised as “what the law is”). This blocks the common pattern where fluent text substitutes for legal grounding.

5) Adversarial review as a required phase
* Red-team the draft for: - bias/disparate impact, - due process problems, - loopholes, - and incentives/gaming. Ethics and governance recommendations explicitly call for impact assessment, auditability, and due diligence mechanisms. OHCHR<ref>{{cite web|title=OHCHR|url=https://www.ohchr.org/sites/default/files/2022-03/UNESCO.pdf|publisher=ohchr.org|access-date=2025-12-28}}</ref>

6) Disclosure to the public
* When AI is used in lawmaking or rulemaking support, require disclosure: - where it was used, - what model/version, - what prompts/constraints, - and which sections were AI-suggested vs. human-authored. This is a legitimacy safeguard: people can contest “machine-shaped” reasoning.

7) Version freezing and reproducibility
* For any ongoing regulatory use, freeze the model version and configuration, or you risk “moving law” because the model changed.