Factual Integrity
Whether the model's statements correspond to verifiable reality: data, documents, measurements, and independent sources.
Challenge
AI Credibility Challenge (AIC)
A global systems challenge — where machine claims must earn their authority.
AI credibility is not a single score. It is the alignment between what a model claims, how it reasons, and how its outputs can be verified.
Challenge AIC reads credibility as a multi-dimensional system property, not a matter of trust or intuition.
Provenance Integrity
Where the model's knowledge comes from: training sources, citations, traceability, and the chain of evidence behind each claim.
Calibration & Consistency
Whether the model behaves predictably: same question → same reasoning → same answer, across runs and contexts.
Bias & Fairness
Systematic distortions in outputs: demographic bias, framing bias, omission bias, and domain imbalance.
Robustness & Stability
How the model behaves under pressure: adversarial prompts, edge cases, ambiguity, and conflicting information.
Cognitive Gaming
Three analysis modes — Cohesion, Truthfulness, Quantification — turn credibility evaluation into a playable sequence of choices.
Play the modes →Hexagonal Cognition Engine
Six-direction semantic analysis: each edge and apex carries a thinking direction, revealing how a claim holds up under conceptual pressure.
Explore the hexagon →Categorised 3D Vocabulary
Structured meaning — tags placed in a hierarchy of domain, scope, and rod — so credibility becomes measurable rather than subjective.
See the vocabulary →Machine Validation Modules
Eight dimensions of machine credibility: factuality, calibration, provenance, bias, robustness, consistency, coverage, and stability.
View modules →Agentic Validation APIs
Traceability, reproducibility, and alignment: every score carries the source, the method, and the run that produced it.
View validation →1. Claim
What the model — or HEUREKA itself — asserts.
2. Measurement
The dimensional method that makes the claim checkable.
3. Evidence
The reproducible result anyone can inspect.
AI Credibility Datasets
What evidence exists that the measurements are reproducible?
- Claim: credibility scores mean something.
- Measurement: every score carries its source, method and run.
- Evidence: datasets under validation — same input, same score, independently checkable.
Pilot Gallery
What does a dimensional measurement actually look like in practice?
- Claim: dimensional AI sees what conventional inspection misses.
- Measurement: one image, one number, one sentence.
- Evidence: fundus and metal-surface workflows you can run on your own images.