# The Decision Proof Gap

Canonical: https://www.meshqu.com/research/decision-proof-gap

AI governance frameworks describe how decisions should be made — but the moment a decision is actually executed, the evidence to defend it almost never exists in a form anyone can verify.

Published: 2026-05-06T00:00:00.000Z

## Key facts

- 81% vs 24% — 81% of financial-services firms run AI in production, while only 24% of regulators collect structured AI data and 5% collect bias data. (The asymmetry the paper is built on, drawn from a survey of 628 organisations across 151 jurisdictions conducted in late 2025 and early 2026. The most regulated sector on earth has limited supervisory visibility into deployed AI.)
- 40% · 52% — 40% of firms are in advanced deployment and 52% are actively running agentic AI — decisions cascading between machines, above the layer any of the existing controls reach.
- 65% — Two-thirds of firms do not monitor their models for bias, and half use no explainability methods at all. (The paper's worked scenario turns on this: a regulator asking a bank to demonstrate fairness across a quarter of AI-driven credit decisions gets a narrative response, because on the firm's side half cannot explain the decisions and on the regulator's side three-quarters do not collect the data that would make the question answerable.)
- 38% vs 18% — No two stakeholder groups agree on who is liable for AI harm: 38% of regulators say the regulated firm should bear it, only 18% of industry agrees, 35% want case-by-case attribution and 22% favour shared liability. (The paper reads the fragmentation as the gap restated. When no party can produce a binding record of who decided what against which authority, every stakeholder rationally reaches for a different theory of liability.)
- 2,972 companies — Outside finance the shape repeats: references to policies, committees and high-level oversight appear more frequently than evidence of operational controls, dedicated resources, escalation pathways or monitoring mechanisms. (The same report calls three times for a shift from disclosure of statements of intent to verifiable practice, and never defines verifiable technically. The paper's observation is that the word does the rhetorical work while the engineering is missing.)
- The four leading candidate solutions fail by construction rather than by immaturity — explainability, logs, governance frameworks and model cards each sit before the decision boundary or after it, never at it.

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