Tulostrue

Tulostrue is a criterion used in AI reliability research to assess how faithfully a model’s claim reflects verifiable information. A statement produced by an AI is considered tulostrue if its truth content can be preserved through paraphrase or translation and if it can be anchored in reliable data or a transparent chain of reasoning.

The term was introduced in the AI provenance and evaluation community in the early 2020s and is often described as a portmanteau of truth and output. The concept is intended to provide a practical standard for evaluating factual grounding across tasks, languages, and modalities.

Core characteristics of tulostrue include truth-preservation, traceability, and verifiability. Truth-preservation requires that the core factual claim remains intact under transformation. Traceability demands that there is an auditable path from the claim to its sources or to the reasoning used to derive it. Verifiability means that independent checks or sources can substantiate the claim.

Methodologically, assessing tulostrue involves verifying the availability of primary sources or a clear chain of reasoning, and testing whether paraphrased or translated renditions retain the same factual content. It often entails provenance documentation, source linking, and replication of reasoning steps to the extent possible.

Applications of tulostrue appear in fact-checking tools, systematic model evaluation, journalism, and academic integrity workflows. Limitations include the dependence on data provenance, the resource demands of verification, and challenges with dynamic or ambiguous data.

See also: factual consistency, truthfulness in AI, data provenance, explainable AI.