Generative Responsible AI Data Evaluation Schema (GRAIDES) for AI Assurance in Local Government

Trust in the application of generative Artificial Intelligence (AI) relies on well-governed measurable evidence of performance and safety. In practice, however, evaluation data is often fragmented across systems, inconsistently structured and difficult to compare. We introduce the Generative Responsible AI Data Evaluation Schema (GRAIDES) as a lightweight open-source data model for centralising AI observability across popular vendors. Practical blueprints for code, architecture and statistical evaluation are shared as guidance about how to approach generative system assurance at the organisational level. Illustrative case study results are reported from Westminster City Council's AI catalogue with a focus on measuring human-model alignment including detecting systematic disagreement between evaluators. By framing evaluations as a data modelling problem, GRAIDES provides a practical pathway toward more consistent and reproducible benchmarking, tuning and assurance activities for generative AI systems.

AI Assurance in UK Defence: Challenges in Operationalising JSP 936

This report examines practical challenges in operationalising JSP 936 Part 1 for AI assurance in UK Defence. Using a structured interpretive review of the directive's requirements, the analysis identifies eight thematic challenge areas adequacy of evidence and argument, management of human interaction with AI, definition of the operational environment, integration of AI within systems of systems, assessment and maintenance of AI performance, analysis of safety and security, measurement of ethicality, and mitigation of the inherent complexities of AI. The report argues that JSP 936 provides a useful governance basis, but that implementation depends on unresolved technical, organisational, and assurance questions. These challenges stem from the socio-technical nature of AI-enabled systems, uncertainty in real-world deployment contexts, limitations in current assurance methodologies, and tensions between performance, safety, human oversight, security, and ethical acceptability. The report identifies areas where further methods, guidance, and organisational capability are needed for the ambitious, safe, and responsible adoption of AI across Defence. This is consistent with MOD's own framing of JSP 936 as requiring iterative implementation and supporting guidance.