• integrated_82#Q2.2

In the RAIDT context, Responsible AI means more than adherence to broad ethical principles. It is the normative starting point that is translated into evidence-based governance for one configured use of GenAI in organisational work. The papers define RAIDT as a run-level evidence framework and governance method whose purpose is to move from principles and policy statements to inspectable proof about how GenAI was actually used. In other words, Responsible AI in RAIDT is not left as aspiration; it is operationalised.

This operationalisation begins by treating the run as the unit of governance. A run is one configured use of a GenAI system for a specific task, at a specific time, in a specific context. RAIDT then asks what evidence must exist so that this use can later be reviewed by someone who was not present. The answer is a run-level evidence pack and a score profile assessed across the five pillars (Responsibility, Auditability, Interpretability, Dependability, Traceability). Those pillars recast familiar Responsible AI concerns into governance outcomes that can be inspected, compared, and improved.

The anchors 1=missing / 3=partial / 5=audit-ready are central here. They convert general commitments such as transparency, accountability, safety, and oversight into measurable judgements about evidentiary quality. Responsible AI in RAIDT therefore means ethically and organisationally appropriate GenAI use that is reviewable in practice, not merely defensible in principle. It is a governance method rather than a slogan, a model-card variant, or a single software tool.

In a healthcare note-summarisation run, Responsible AI in the RAIDT sense is evidenced when the record shows that the system was prompted conservatively, uncertainty and escalation guidance were required, and human oversight was flagged for high-risk content. The organisation does not merely claim that safety matters; it captures the controls and the resulting output in a reviewable form.

That run can then be scored. If oversight steps are missing, responsibility and auditability would fall. If the summary is readable but does not communicate uncertainty, interpretability may remain partial. If the organisation can show the full prompt, output, and review decision, the case moves closer to audit-ready governance. The practical significance is that Responsible AI becomes visible in the evidence of one use rather than remaining a general aspiration about the system as a whole.

• 11-RAIDT_Academic_Logic_M_v11
• 15-RAIDT-IS-Governance_M_v07