AI Standards Mapping

1. Hypothesis

An explicit, testable and continuously evidenced approach to ai standards mapping, with clear ownership, independent review, runtime telemetry and recovery, will outperform policy-only or periodic compliance approaches.

2. Experiment design

Design: Multi-site institutional governance study focused on AI Standards Mapping, combining baseline maturity assessment, controlled implementation, live telemetry review and post-incident or post-exercise evaluation. Methods: regulatory mapping; standards crosswalk; gap assessment; conformity testing; legal analysis; horizon scanning; stakeholder interviews; document and control review; fault and incident simulation; longitudinal implementation assessment; methods adapted specifically to AI Standards Mapping Independent variables: control design; ownership clarity; review independence; telemetry coverage; enforcement level; organisational maturity; system risk Dependent variables: coverage; compliance gap; conformity result; update latency; jurisdictional conflict; certification readiness; incident impact; recovery time; stakeholder confidence Confounders: sector; scale; legal context; legacy systems; budget; workforce capability; procurement model; incident history; political and public pressure Measures: coverage; compliance gap; conformity result; update latency; jurisdictional conflict; certification readiness; control coverage; implementation fidelity; exception rate; subgroup and rights impacts; stakeholder comprehension; cost and time to evidence Success criteria: Material improvement in measured governance outcomes; clear ownership and authority; controls operate as intended; evidence is complete and reproducible; exceptions are bounded; no disproportionate rights harm; recovery and learning are demonstrated. Failure conditions: No measurable improvement; controls exist only on paper; evidence is missing or stale; authority is unclear; telemetry cannot observe execution; exceptions become routine; recovery fails; costs or harms exceed public value.

3. Seed result / current evidence

DEFENSIBLE SEED RESULT — NON-EMPIRICAL. The current evidence supports AI Standards Mapping as a testable research proposition. Problem basis: AI Standards Mapping is often described in policy or documentation but not consistently implemented, observed or evidenced at runtime, creating gaps between institutional claims and actual behaviour. Directional expectation: If supported, the approach should improve coverage; compliance gap; conformity result; update latency; jurisdictional conflict; certification readiness, reduce control drift and incident impact, and increase justified stakeholder confidence. Proposed observations: coverage; compliance gap; conformity result; update latency; jurisdictional conflict; certification readiness; control coverage; implementation fidelity; exception rate; subgroup and rights impacts; stakeholder comprehension; cost and time to evidence. Seed data profile: Evidence Strength 10/100; Confidence 25/100; Maturity 20/100; Overall Health 35/100; Novelty 75/100; Strategic Importance 95/100. Evidence boundary: No validated results yet.; experiments 0, studies 0, participants 0. This is suitable for protocol formation and baseline comparison, not as a finding of effect.

4. Seed conclusion

DEFENSIBLE SEED CONCLUSION — PROVISIONAL. AI Standards Mapping warrants structured testing because the CSV identifies a defined problem, falsifiable hypothesis, measurable outcomes and relevant literature foundations. The present position is that “An explicit, testable and continuously evidenced approach to ai standards mapping, with clear ownership, independent review, runtime telemetry and recovery, will outperform policy-only or periodic compliance approaches.” is plausible and decision-relevant, but unvalidated. Proceed to controlled testing against the stated success and failure conditions. Confirm, narrow or reject this seed after effect sizes, uncertainty, subgroup outcomes, adverse effects, persistence and handback performance are observed.

Prior-art search performed before starting

PRIOR-ART SEED BASELINE — PARTIAL. The CSV records these literature domains: Institutional governance; assurance; audit; administrative law; public-sector management; risk and safety engineering; ethics; standards and regulation; literature specific to AI Standards Mapping. It also records: EU AI Act — https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai; ISO/IEC JTC 1/SC 42 — https://www.iso.org/committee/6794475.html; NIST AI RMF — https://www.nist.gov/itl/ai-risk-management-framework; Australian Government AI policy — https://www.industry.gov.au/. Evidence register status: “Seeded; authoritative source register initiated; institutional evidence not yet ingested”. This is defensible as a starting prior-art inventory, but not as proof of a completed systematic search because search dates, databases, exact queries, reviewer, result counts, screening decisions, claim mapping and a replayable receipt are absent.

Prior-art material named: Existing literature: Institutional governance; assurance; audit; administrative law; public-sector management; risk and safety engineering; ethics; standards and regulation; literature specific to AI Standards Mapping. References: EU AI Act — https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai; ISO/IEC JTC 1/SC 42 — https://www.iso.org/committee/6794475.html; NIST AI RMF — https://www.nist.gov/itl/ai-risk-management-framework; Australian Government AI policy — https://www.industry.gov.au/

Critical gap / next action

Create and attach a dated prior-art search log; lock the protocol; execute the proposed study; link raw data and analysis; then replace the results and conclusion placeholders with evidence-bounded findings.