'People get hurt from the very practical ways [large language] models fall short in deployment, & these failures are the result of choices made by the builders of these systems - choices we are obliged to critique and hold model builders accountable for.'
And ongoing asymmetries of blame and praise persist. Model builders and tech evangelists alike attribute impressive and seemingly flawless output to a mythically autonomous model, a supposed technological marvel. The human decision-making involved in model development is erased, and a model’s feats are observed as independent of the design and implementation choices of its engineers.
But without naming and recognizing the engineering choices that contribute to the outcomes of these models, it’s almost impossible to acknowledge the related responsibilities. As a result, both functional failures and discriminatory outcomes are also framed as devoid of engineering choices—blamed on society at large or supposedly “naturally occurring” datasets, factors the companies developing these models claim they have little control over.
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