AI System Assurance

Definition

AI System Assurance is the systematic design and evaluation of ai system assurance within the structured demonstration that AI systems are sufficiently safe, reliable, lawful and fit for their intended purpose.

Why this matters

Weak ai system assurance 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 system assurance 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 system assurance, with clear ownership, independent review, runtime telemetry and recovery, will outperform policy-only or periodic compliance approaches.

Proposed methods

assurance case analysis; model and system testing; hazard analysis; independent review; control validation; red-team exercises; stakeholder interviews; document and control review; fault and incident simulation; longitudinal implementation assessment; methods adapted specifically to AI System Assurance

Stakeholders and beneficiaries

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