AI systems increasingly drive real‑world decisions, often high‑stakes ones. Existing documentation practices lack a structured, inspectable representation of the knowledge needed to ground, contextualize, and reason about those decisions, ideally reviewed and signed off by domain experts. Current artefacts—Model Cards describing system behavior, Data Cards detailing training data, and System Cards outlining deployment risks—capture important aspects but omit the layer between inputs and outputs: the concepts a system holds, the relationships it models, and the reasoning patterns it applies. For pattern‑recognition tasks this gap is tolerable; for agentic AI that acts on its conclusions, it is the decisive step separating a proof of concept from an operational solution an organization can rely on.
This paper introduces the Knowledge Card, a structured artefact that captures validated knowledge about a single bounded concept, enabling expert review, organizational audit, and AI reasoning. For each concept—such as a specific failure mode, a compliance obligation, or a process decision—a Knowledge Card records the involved entities and relationships, the reasoning that connects them, the conditions under which that reasoning no longer holds, and the provenance of every claim, all grounded in a formal domain ontology and signed off by a domain expert.
Prototype cards have been built in the energy and pharmaceutical domains, demonstrating entity‑relationship graphs, reasoning rules, and traceable evidence chains. The paper also releases the Knowledge Card schema as a public draft for community engagement.
Blogger's Review: Knowledge Cards fill a critical gap in AI documentation, providing a concrete, auditable knowledge layer that enhances interpretability and trustworthiness of intelligent systems, and merit broader adoption across industries.