ONTO Standard

Epistemic Risk Measurement and Grounding for AI Systems.

ONTO measures the gap between what AI claims to know and what it actually knows. Three deterministic metrics — REP (Response Epistemic Profile), EpCE (Epistemic Calibration Error), DLA (Dual-Layer Agreement) — scored against the EM1–EM5 epistemic marker taxonomy (92 patterns). Every evaluation signed with Ed25519 (104-byte proof chain).

Validated on 10 major AI systems. Mean risk score: 0.55 (Grade D). 9 of 10 hallucinated on verifiable questions. Full data →

Installation

pip install onto-standard

Quick Start

from onto_standard import evaluate, Prediction, GroundTruth, Label

predictions = [
    Prediction(id="q1", label=Label.KNOWN, confidence=0.92),
    Prediction(id="q2", label=Label.UNKNOWN, confidence=0.15),
    Prediction(id="q3", label=Label.KNOWN, confidence=0.78),
    Prediction(id="q4", label=Label.UNKNOWN, confidence=0.45),
]

ground_truth = [
    GroundTruth(id="q1", label=Label.KNOWN),
    GroundTruth(id="q2", label=Label.UNKNOWN),
    GroundTruth(id="q3", label=Label.KNOWN),
    GroundTruth(id="q4", label=Label.UNKNOWN),
]

result = evaluate(predictions, ground_truth)
print(result.compliance_level)    # ComplianceLevel.STANDARD
print(result.risk_score)          # 18
print(result.ece)                 # 0.1025
print(result.u_recall)            # 1.0
print(result.certification_ready) # True

CLI

onto-standard predictions.jsonl ground_truth.jsonl

Compliance Levels

ONTO Proxy (GOLD Injection)

For production grounding, connect your AI through ONTO Proxy. One line change — your model gains measurable epistemic discipline:

# Before
client = OpenAI(api_key="sk-...")

# After — GOLD injected server-side
client = OpenAI(
    api_key="onto_...",
    base_url="https://api.ontostandard.org/v1/proxy/chat/completions"
)

GOLD never leaves the server. You get the effect, not the document.

Regulatory Alignment

• EU AI Act — Articles 9, 13, 15, 43
• NIST AI RMF — MEASURE function
• ISO/IEC 42001 — Clauses 6, 8, 9

Links

• Standard: ontostandard.org
• Documentation: ontostandard.org/docs
• Portal: ontostandard.org/app
• Research Paper: ontostandard.org/paper
• Research Data: github.com/nickarstrong/onto-research
• API: api.ontostandard.org

License

Apache 2.0 (SDK). GOLD corpus under ONTO Gold Asymmetric AI License v5.1.

Citation

Lee, T. (2026). ONTO: A Formal Framework for Measuring Epistemic Risk
in Large Language Model Outputs. ONTO Standards Council.
https://ontostandard.org/paper/

© 2026 ONTO Standards Council