Data ownership in AI is often spoken about as if it is already clearly defined, but in practice it still feels unsettled. Attribution systems like OpenLedger bring structure to something that has historically been vague. They make contribution visible, they map where data flows, and they introduce a level of transparency that was missing for a long time.
But visibility alone does not fully translate into ownership. The legal and ethical meaning of control over data is still separate from the technical ability to trace it. Blockchain can record origin and movement with precision, yet it does not automatically resolve questions of permission, withdrawal, or long-term rights.
There is also a deeper mismatch between permanence and privacy frameworks. Systems built for immutable records do not easily align with expectations shaped by modern data protection laws. At the same time, most of the data powering current AI systems already exists outside any clean consent structure, which makes the challenge even harder to address in hindsight.
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