References

What the model is built on

The frameworks, standards and evidence behind each of the three dimensions.

Frameworks for accounting proactively for social and ecological impact.

Map, measure and manage AI risks, including social impacts, as an iterative, context-dependent practice.

Lifecycle ethics-by-design, impact mapping, diverse participation and mitigation planning.

Lifecycle accountability and stakeholder safeguards for trustworthy AI.

Ethical and governance risks specific to clinical, public-health and health-system deployments.

ILO

AI reshapes work, productivity, transition pressures and the distribution of benefits and burdens.

KAPOR

An equity-centered agenda for AI: who builds it, who it affects, and what accountability to impacted communities requires.

The standards that the safety and accountability pillars are checked against.

Conformity assessment, technical documentation and post-market monitoring across the four risk tiers.

Maps generative AI risks to the engineering mitigations that counter them, with a measurement loop.

Policy integration, lifecycle management, repeatable risk assessment and management review trails.