Auditing Not-Knowing: The Universal Ignorance Audit and the Epistemology of an AI-Assisted Research Pipeline

Every field of inquiry is defined as much by what it does not know as by what it knows — yet not-knowing is rarely examined with the rigor given to knowing. The Universal Ignorance Audit (Quni-Gudzinas, 10.5281/zenodo.21901984) is a fifteen-question method that makes the structure of a field's ignorance surveyable: its scope, its kinds, its sources, and its tractability. The audit treats ignorance not as a homogeneous void to be filled but as a structured landscape with distinct regions — known unknowns, unknown unknowns, and ignorance about ignorance itself — each demanding a different epistemic response.

This abstract presents the audit together with the epistemic lessons of an AI-assisted research pipeline spanning physics, mathematics, and philosophy (Quni-Gudzinas, 10.5281/zenodo.21901983). The pipeline's core finding: AI assistance does not merely accelerate inquiry; it reorganizes the ignorance landscape of a research program. Machine-generated results introduce a new class of not-knowing — whether a result is machine-generated, whether it is correct, and whether its apparent coherence conceals gaps. The companion work therefore pairs ignorance auditing with AI-generation detection: not as a policing instrument, but as a precondition for trust in hybrid human-machine inquiry.

Three lessons generalize across disciplines. First, legibility: an audit renders what a program does not know explicit enough to be checked by others, converting private doubt into public, testable structure. Second, verification: in AI-assisted inquiry, generation and verification must be separated in the workflow, because the agent that proposes results cannot be the sole judge of them. Third, humility as method: the audit's questions are deliberately answerable with "we do not know," making bounded ignorance an admissible, even routine, outcome of research rather than a failure to be hidden.

The philosophical claim is a unification thesis. Diverse forms of inquiry differ in objects, methods, and standards, yet share a common structure of not-knowing: the same fifteen questions apply in any field, and their answers travel. A physicist's unknown-unknowns, a mathematician's proof-gaps, and a philosopher's conceptual confusions are regions of one landscape. The audit thus offers a shared grammar in which disciplines can state what they do not know to one another — a unification not by method or ontology, but by the structure of ignorance itself. In an era of AI-assisted science, where the provenance and reliability of results become newly uncertain, this common grammar is a condition for cross-disciplinary trust.