Track the implementation and effectiveness of due diligence: identify impacts overlooked before; reassess whether new risks exist or accepted risks are no longer acceptable; evaluate stakeholder engagement for timeliness, accessibility and safety; feed lessons learned back; monitor AI system performance or assurance criteria under deployment-like conditions by documenting TEVV test sets, metrics and tools, documenting measured improvements or declines with stakeholders, and documenting and sharing incident and monitoring information with affected communities, governments, workers, unions, civil society and academia; review or audit internal commitments periodically; and assess business relationships periodically to verify mitigation.
The graph holds this control, the 0 it maps to, and the evidence behind each claim, over MCP and REST.