The missing layer in AI transparency: From content marking to machine-readable data governance
A piece published through the IAPP argues that current AI transparency efforts focus too narrowly on labeling AI-generated content, while missing a deeper layer: machine-readable standards that govern how data is collected, used, and shared within AI systems themselves.
Why this matters: Slapping a label on AI-generated content is the easy part. It tells you something was made by a machine. It tells you nothing about what data trained it, where that data came from, or who had permission to use it. That missing layer is where the real accountability lives. If governance rules are not readable by machines, they cannot be enforced at scale. You end up with transparency theater: visible enough to satisfy a regulator, invisible enough to protect no one.
Who should care: General readers · AI governance · Policy
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