AI Standards Mapping

Definition

AI Standards Mapping is the systematic design and evaluation of ai standards mapping within the mapping, adoption and operationalisation of standards, laws, certification and regulatory expectations.

Why this matters

Weak ai standards mapping can lead to unsafe deployment, unlawful or unauthorised action, wasted public resources, loss of rights, poor accountability and declining institutional trust.

Research questions

Which controls, evidence and institutional arrangements make ai standards mapping effective in practice, and how do outcomes vary by sector, system risk, organisational maturity and operating context?

Hypotheses

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.

Proposed 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

Stakeholders and beneficiaries

citizens; public servants; executives; boards; regulators; auditors; legal and risk teams; technology teams; service users; civil society; suppliers