Responsible AI Assessment
The Hub's capability to assess safety, fairness, and accountability of deployed models – with the highest bar for clinical and high-stakes national use cases.
The Hub assesses the responsibility of the AI it hosts: whether a model is safe, fair, accountable, and appropriate for the population it serves. For national use cases touching health, finance, and education, responsible-AI assessment is a deployment gate, not a nice-to-have.
What responsible-AI assessment covers
- Safety – will the model cause harm if it is wrong? For clinical use for example, the failure mode is patient harm, so clinical fine-tuning, RLHF / DPO alignment, and safety evaluation are non-negotiable pre-deployment gates.
- Fairness & inclusion – does the model perform equitably across languages, regions, genders, and dialects, rather than only for well-represented groups?
- Accountability – who is responsible for outputs, and how are errors surfaced, escalated, and corrected?
- Data protection – alignment with NDPA and consent for sensitive health and financial data.
- Human oversight – where a human must stay in the loop (e.g. clinical decisions, programme allocation).
Tooling
DSN's GenAIGov platform turns responsible-AI principles into a measurable, scored self-assessment across the eight-part STANDARD framework – a concrete instrument the Hub can require or recommend before a use case is deployed.
AI Governance
How the Hub governs a shared national compute resource – workload quotas, priority tiers, access control, scheduling policy, and data sovereignty.
Governance & Risk
How the Hub governs a shared national resource – workload management, data governance, access control, and the safety gates that manage risk before deployment.