Agencies and sectoral regulators formulating AI policy back innovation, adoption and the spread of benefits while dealing with risks through policy instruments; keep frameworks flexible and agile with periodic review, monitoring and recalibration on stakeholder feedback; give priority to instruments where harm is real and present or life, livelihood or well-being; avoid compliance-heavy requirements such as mandatory approvals or licensing unless necessary; determine the most useful, relevant and least burdensome instrument for the objective, whether industry codes, technical standards, advisories or binding rules; and encourage techno-legal approaches to privacy, cybersecurity, fairness and transparency where such policy measures already exist.
The graph holds this control, the 0 it maps to, and the evidence behind each claim, over MCP and REST.