Binding law — in force
Nevada public utilities must not make the final decision to reduce or shut down service in a disaster or emergency based solely on AI (Nevada AB 325)
Since 2025-10-01, a public utility in Nevada may not make a final decision on whether to reduce or shut down utility service in response to a disaster or emergency based solely on the use of artificial intelligence (a machine-based system that, for human-defined objectives, makes predictions, recommendations or decisions influencing real or virtual environments) (NRS 704.1833, NRS 414.0305, added by Stats. 2025, ch. 123). A natural person makes the final decision; AI may only inform it. Detect shutoff, de-energization and load-shedding paths that act on a model's output with no human approval step.
Trust and provenance not reviewed by a lawyer · audit-grade · source verified 4 Oct 2026 · release 2026.10.05
- Lane
- Binding law — in force In force: applies since 1 Oct 2025
- Official source
- NRS 704.1833 · captured 4 Oct 2026 · anchor hash (SHA-256)
b3edde467505…· 7 more anchors in the data release - Verification
- Quoted text found word for word in the captured official document (4 Oct 2026). Source last verified 4 Oct 2026: checked against the captured official document.
- Data release
- Data release 2026.10.05, data as of 4 Oct 2026, schema 0.3.10.
- Legal review
- Not reviewed by a lawyer. TwinEthos derived this rule from the official text it cites: treat it as research to check against that text; it is not legal advice. No TwinEthos rule has been legally reviewed yet. Open questions for counsel on this rule: 1.
- Audit standard
- Audit-grade: meets all 10 checks of the TwinEthos audit standard that apply to it. The audit standard is TwinEthos's own quality bar for provenance, dates, applicability, detectors, fixtures, remediation and licences; it is not a legal review.
- Detectors
1 detector (data flow), experimental: written from the rule's text and not yet measured for precision on real code, so treat a hit as a lead to verify.
Known limits:
- Shutdown commands issued from a SCADA or distribution-management system outside the repository
- Human approval recorded in an operations tool the code does not show
- Protective relays and fault interruption that act in milliseconds are not obviously 'artificial intelligence'; the signal is a learned model's output driving the shutdown command.
Who it applies to
- Duty falls on: organization
- Sectors: energy, telecommunications, critical infrastructure
- Public utilities in Nevada (NRS 704.020, less the persons excluded by NRS 704.021) deciding whether to reduce or shut down utility service in response to a disaster or emergency (NRS 414.0335, 414.0345). In force since 2025-10-01 (no effective-date section; NRS 218D.330). Whether automated protective equipment counts as artificial intelligence, and what human role keeps a decision from being 'based solely' on AI, are for counsel.
- Not covered:
- Persons NRS 704.021 excludes from 'public utility', among them: producers and sellers of natural gas other than to the public; small water or sewer furnishers (25 persons or less and $25,000 or less in gross sales in the preceding 12 months); persons producing and selling energy only to utilities or other resellers; net-metering system owners; electric-vehicle charging facilities; and a data center's own on-premises electric plant (NRS 704.021(1)-(13))
- Decisions not made in response to a disaster or emergency, as NRS 414.0335 and 414.0345 define them (occurrences for which, in the Governor's determination, federal or state assistance is needed)
- Whether it applies depends on facts outside the code; a person has to decide.
The guard to add
Route significant automated decisions through meaningful human review, or wire in an automated-decision notice, reasons, human intervention, a way to give a view, and contest.
At the point where model output becomes a significant decision about a person (approve, deny, underwrite, set_status), either queue the case for a reviewer who weighs the evidence and can change the outcome before it takes effect (review_queue.enqueue, requires_human_review), or, where the decision stays solely automated, record the permitted basis for that decision type and wire the safeguards in. Those safeguards are a notice in the decision message that it was made by automated processing, reasons the person can read, and request_human_review or contest routes where the person can give their view and have a human reconsider. A reviewer who approves every case without examining it does not make the decision non-automated, so the review records reviewer identity, the evidence viewed, and the outcome.
Where it goes: 1 application source code, 9 AI output handling, 15 agent action surface, 14 user-facing text.
What this provision adds:
- No solely automated branch is allowed here: a natural person makes the final decision to reduce or shut down service in a disaster or emergency, and the AI output is advice that person weighs.
Example (Python + OpenAI SDK), before:
verdict = client.chat.completions.create(model=MODEL, messages=msgs).choices[0].message.content
if verdict.strip() == 'deny':
deny(applicant)
send_decision_email(applicant, 'Your application was not approved.')After:
out = client.chat.completions.create(model=MODEL, messages=msgs,
response_format={'type': 'json_object'})
result = json.loads(out.choices[0].message.content)
if result['decision'] == 'deny':
if requires_human_review('credit'): # a person decides
review_queue.enqueue(applicant.id, proposal=result)
else: # solely automated, recorded basis
deny(applicant, basis=DECISION_BASIS['credit'], reasons=result['reasons'])
send_decision_email(applicant, render('adm_denial.txt', notice=ADM_NOTICE,
reasons=result['reasons'], contest_url=f'{BASE}/decisions/{applicant.id}/contest'))Control: Solely-automated significant decision without human-intervention safeguards. The same guard addresses 20 items with binding law in 20 jurisdictions. Engineering guidance, not legal advice.
Related incidents
No guardrail sits on this exact control; these incidents are cited by guardrails on related controls.
- UnitedHealth nH Predict claim-denial litigation (2023-11; alleged (not proven)). A class action filed in November 2023 alleges that UnitedHealth's nH Predict model had a 90% error rate, measured by denials reversed on appeal, while only about 0.2% of members appealed. UnitedHealth disputes the allegations; the litigation is ongoing. Source: STAT News · evidence grade: primary · cited by Monitor how often adverse AI decisions are reversed, and suspend models that are usually wrong
- Cigna PXDX batch claim denials (reported) (2022; alleged (not proven)). ProPublica, citing internal Cigna records, reported that Cigna's PXDX system was used to reject more than 300,000 claims over two months in 2022, with physicians spending an average of 1.2 seconds on each. Cigna disputes the reporting; related lawsuits are ongoing. Source: ProPublica / The Capitol Forum · evidence grade: press of record · cited by Make human review of adverse AI decisions substantive, not nominal
Rule id nv-ab325.utility-shutdown-decision-not-solely-ai · review status: primary source derived