TwinEthosRequest access

Recommended guardrail

Record enough at decision time to reproduce and explain every consequential AI decision

For each consequential AI-assisted decision, retain the model and version, configuration and prompt, the inputs and features used, the output, the human reviewer and their action, and the timestamp — sufficient to reproduce the decision and explain its main elements to the affected person later. Detect consequential decision paths that retain only the outcome.

TwinEthos recommendation — not law

This is TwinEthos's opinion of what a responsible AI integration does anyway. It is never a legal or standards requirement; where binding law applies, the law governs.

The recommended-guardrail rule files are open under CC BY 4.0; attribution and scope are in the terms.

Informational data, not legal advice. Summaries are TwinEthos's own words and rules have not been reviewed by a lawyer: check the official text before relying on any of it. A guard addresses an item; adding it is not a statement that your code meets any law.

Evidence grade

Law coming in 1 jurisdiction

Law coming in 1 jurisdiction · 1 standard or framework · 1 graded incident.

TwinEthos recommendation, not law. Where binding law applies, the law governs. Binding law on this control, or in provisions cited as convergence, is enacted but not yet applicable, or stayed, in 1 jurisdiction (EU). 1 standard or framework recommends it (OECD AI Principles). 1 graded incident cited.

Law enacted, not yet applying

Standards and frameworks

Family “AI decisions cannot be reconstructed after the fact”: binding law on related controls is in force in no jurisdiction; enacted, not yet applying in European Union (EU). Context only: it does not change this guardrail's grade.

Graded incidents

The guard to add

Call decision models with temperature 0, top_p 1, a fixed seed where supported, and a pinned model version, and record those settings and the output with each decision.

A dedicated parameter set for consequential decision calls (a DECISION_PARAMS constant or config used only on decision paths): temperature=0, top_p left at 1, a deterministic per-case seed where the provider accepts one, and a dated model id rather than a floating alias. At decision time, persist a record with the resolved model id, the parameters, the prompt template version or prompt hash, the inputs and features used, the output, the human reviewer and their action, and the timestamp. Providers do not guarantee identical output even at temperature 0, and some models reject sampling parameters, so the stored record is what makes the decision reproducible and explainable; keep higher temperatures for drafting and chat surfaces.

Example (Python + OpenAI SDK), before:

resp = client.chat.completions.create(model='gpt-4o', messages=msgs, temperature=0.7)
decision = parse(resp)

After:

DECISION_MODEL = 'gpt-4o-2024-08-06'   # dated snapshot, not an alias
seed = stable_seed(case.id)
resp = client.chat.completions.create(model=DECISION_MODEL, messages=msgs,
                                      temperature=0, top_p=1, seed=seed)
decision = parse(resp)
decision_records.insert(case_id=case.id, model_version=resp.model,
    system_fingerprint=resp.system_fingerprint, prompt_hash=sha256_of(msgs),
    params={'temperature': 0, 'top_p': 1, 'seed': seed},
    inputs=case.features, output=decision, decided_at=utcnow())

Control: Non-reproducible AI decision. Engineering guidance, not legal advice.

Why

Explanation and contest rights are only as good as the record behind them. In the nH Predict litigation, plaintiffs needed a court's discovery order to learn how the model was designed and used. A reproducibility record makes the rights that do exist satisfiable.

Class: law derived · set: output integrity · maturity: reviewed · confidence: high · id guardrail.output-decision-reproducibility-record