The Three Laws¶

Operating principles

Three laws for decision-grade AI.

These are not slogans. They translate directly into architecture: evidence gates, constraint enforcement, and accountable decision ownership.

Methodology Governance approach

Law 1: No answer without evidence

If the system can’t point to a source, it should say “I don’t know”.

Evidence is a gate: it prevents plausible-but-wrong claims from entering high-stakes workflows.

Implementation requirements

Outputs carry citations/provenance (document, section, timestamp, version).
Claims are separated into facts vs hypotheses vs assumptions.
Missing evidence triggers abstention or escalation.

Law 2: Order before speed

Structure the domain before automating decisions.

The fastest way to ship unreliable AI is to automate first and model the domain later.

Implementation requirements

Define core concepts and relations (what exists, how it connects).
Encode constraints (what must never happen; what is allowed only under conditions).
Version the knowledge layer; treat changes as operational risk.

Law 3: Humans remain accountable

AI assists, simulates, and recommends. Humans own responsibility.

Accountability can be supported by AI; it cannot be outsourced to it.

Implementation requirements

Explicit decision owner per workflow (role, escalation path).
Audit trail: what was proposed, why, what evidence, what constraints, who approved.
Clear separation between “advisor mode” and “action mode”.

Diagram: evidence gate (non-negotiable)

flowchart LR;
    Q["Question / decision"] --> E["Evidence available?"];
    E -->|"No"| A["Abstain / escalate"];
    E -->|"Yes"| V["Verify + trace"];
    V --> O["Output + provenance"];

Diagram: human accountability in the loop

flowchart TB;
    S["System proposes"] --> J["Human judgment"];
    J -->|"Approve"| X["Execute / publish"];
    J -->|"Reject"| R["Revise / request more evidence"];
    X --> L["Log decision + rationale"];
    R --> L;