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White Paper · WP-PROOF-01 · v1.0

The 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.

Why AI governance fails at the moment of execution

Sam Carter6 May 202635 pagesPublic DistributionRead the PDF
Two engineers standing either side of an unbridged gap in a raised walkway

81% of financial-services firms run AI in production, while only 24% of regulators collect structured AI data and 5% collect bias data.

81% vs 24%

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.

Source: CCAF Global AI in Financial Services Report 2026, cited in Executive Summary and §Evidence — Adoption vs Supervision, Fig. 2

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.

40% · 52%
Source: CCAF Global AI Report 2026, Fig. 2, cited in §Evidence — Adoption vs Supervision
Reference
WP-PROOF-01 · v1.0
Extent
35 pages · 12.1 MB
Published
6 May 2026
Classification
Public Distribution

Cite as

Carter, S. (2026). The Decision Proof Gap. MeshQu White Paper WP-PROOF-01. Zenodo. https://doi.org/10.5281/zenodo.20055736

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Read the PDF · 35 pp · 12.1 MB
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Abstract

The paper's own abstract, verbatim — not a rewrite.

AI governance frameworks describe how decisions should be made — but at the moment a consequential decision is executed, the evidence required to defend it almost never exists in a verifiable form. This paper names that structural gap, traces it across regulated sectors, surveys why existing solutions (explainability, logs, policy-as-code, model cards) fail to close it, and proposes the Decision Receipt as the missing primitive: a signed, replayable, portable record bound to the policy in force at the moment of decision.

Key figures

Every figure names the section it comes from, and every one is re-derivable from the published corpus.

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.

CCAF Global AI in Financial Services Report 2026, cited in Executive Summary and §Evidence — Adoption vs Supervision, Fig. 2

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.

CCAF Global AI Report 2026, Fig. 2, cited in §Evidence — Adoption vs Supervision

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.

CCAF Global AI Report 2026, cited in §Evidence — Adoption vs Supervision

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.

CCAF Global AI Report 2026, cited in §Evidence — 'The liability disagreement is the Gap restated'

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.

Thomson Reuters Foundation / UNESCO AI Company Data Initiative, cited in §Evidence — 'Outside finance, the same shape'

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.

§Why Existing Solutions Fail

Cited in this piece

The work this piece rests on — the author’s own list first, then everything it links out to.

  1. 01

How to cite

APA from the paper's own recommended citation; BibTeX derived from the same fields, not authored twice.

APA · from the paper’s own recommended citation
Carter, S. (2026). The Decision Proof Gap. MeshQu White Paper WP-PROOF-01. Zenodo. https://doi.org/10.5281/zenodo.20055736
BibTeX · derived from the same fields
@techreport{carter2026decision,
  author      = {Carter, Sam},
  title       = {The Decision Proof Gap},
  subtitle    = {Why AI governance fails at the moment of execution},
  institution = {MeshQu},
  type        = {White Paper},
  number      = {WP-PROOF-01},
  year        = {2026},
  month       = {5},
  url         = {https://www.meshqu.com/research/decision-proof-gap},
  doi         = {10.5281/zenodo.20055736}
}

The programme in sequence

Each experiment answers the question the one before it left open, with the white paper as the argument they test.

  1. 00

    The Decision Proof Gap You are here

    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.

    White paper · WP-PROOF-01 · 35 pp.
  2. 02

    When AI hedges and policy commits

    We ran 283 real UK procurement decisions through both an AI agent and MeshQu’s policy engine at the same moment, binding every verdict to a signed receipt.

    Research paper · MRP-2026-02 · 26 pp.
  3. 03

    When precedents commit AI and policy pulls it back

    We showed one AI agent the same 283 UK procurement decisions five times, adding more of our governance rules each round — from nothing, up to the full policy.

    Research paper · MRP-2026-03 · 38 pp.
  4. 04

    Precedents, policy, and commitment

    We re-ran the same procurement decisions through an AI agent with the governance context broken apart piece by piece — to find out which piece was doing the work.

    Research paper · MRP-2026-04 · 46 pp.