Unlike small, “narrow AI” models built for specific tasks and trained using curated datasets, systems that are advertised as having “broad general knowledge and domain expertise” require models with upwards of hundreds of billions of parame…As detailed by a number of scholars, both the environmental impacts and the unsafe outputs of these systems disproportionately affect marginalized groups like racial and gender minorities, disabled people, and citizens of developing countri…
Emily Bender
scientist · 2 mentions across 1 reading
In this course
Emily Bender is a computational linguist known for critical work on large language models, particularly their environmental costs and social harms. The excerpts position her scholarship within arguments about how scale in AI systems—the billions of parameters and massive datasets required for "general" models—creates externalized costs that fall disproportionately on marginalized communities, from environmental damage to unsafe algorithmic outputs. Her work appears to ground the seminar's inquiry into how technical decisions in AI development are inseparable from questions of power, harm distribution, and whose interests are served by different architectural choices.
Mentioned in 1 reading
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