Paper 01 · Conceptual definition
Preprint · 2026Auditable Decision Intelligence: A Decision-Centered Definition of Explainable AI for High-Stakes Decisions
A consequential AI decision is explainable when a qualified reviewer can externally reconstruct and contest its source-to-decision record — reconstructable, contestable, governable, and bounded.
In high-stakes AI, explainability is usually treated as the presence of an explanation artifact. We redefine it as an institutional property of a single consequential decision: the ability to reconstruct, contest, govern, and bound its source-to-decision record. The paper specifies the Decision Audit Contract (DAC) — the minimal record that makes one decision auditable — and shows the definition is a genuine contribution, not a relabeling of existing transparency work.
Reconstructable
The decision can be replayed from its recorded source-to-decision path.
Contestable
A reviewer can pinpoint the exact fact, rule, or judgment to challenge.
Governable
The record shows where the system may reason freely and where approval is required.
Bounded
Claims stay inside covered sources, valid context, and stated limits.
Falsifiable test
A system fully compliant with Kroll's accountable algorithms, Cobbe's reviewable automated decision-making, the EU AI Act (Art. 12 & 14), and NIST IR 8312 can still fail ADI — because none guarantees a per-decision source → claim → boundary link.