Research paperUniversity of Washington / Black in AI / Independent2021-03-01
By Emily M. Bender, Timnit Gebru, Angelina McMillan-Major, Shmargaret Shmitchell
A widely cited critique of the trend toward ever-larger language models, examining environmental cost, documentation practices, bias amplification and the risk of models producing fluent but meaningless ("stochastic parrot") text mistaken for understanding.
Why it matters
Published in the ACM Conference on Fairness, Accountability and Transparency in March 2021 - before ChatGPT existed - it anticipated nearly every concern that later became mainstream about scale, bias and misplaced trust in fluent AI output, and remains the paper most cited when those concerns are raised.
Key takeaways
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