Transparency is addressed in both the process (the design and implementation practices behind an outcome) and the product (the content and justification of the outcome): algorithms are developed so that the transparency of inputs is plain and explainable to end-users, with evidence on the data used to reach decisions; affected individuals and communities are informed when an outcome is produced and can request explanatory information from the owner, enabling identification, analysis and audit of decisions; and where a third party builds the system the owner carries out AI ethics due diligence and secures accessible, traceable documentation before procurement or sign-off.
The graph holds this control, the 0 it maps to, and the evidence behind each claim, over MCP and REST.