Person
Timnit Gebru
AI ethics researchers documenting bias in large language models and harms of opaque training data.
Why this matters
Their Stochastic Parrots paper names when interpretation at scale reproduces inequality and erases accountability.
Works3 sources
article
On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?
Bender, Emily M., Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell. "On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?" Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (FAccT), 610-623. https://doi.org/10.1145/3442188.3445922.
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dataset
Datasheets for Datasets.
Gebru, Timnit, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna Wallach, Hal Daumé III, and Kate Crawford. "Datasheets for Datasets." Communications of the ACM 64, no. 12 (2021): 86-92.
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article
Model Cards for Model Reporting.
Mitchell, Margaret, Simone Wu, Andrew Zaldivar, Parker Barnes, Lucy Vasserman, Ben Hutchinson, Elena Spitzer, Inioluwa Deborah Raji, and Timnit Gebru. "Model Cards for Model Reporting." Proceedings of the Conference on Fairness, Accountability, and Transparency (FAccT), 220-229. https://doi.org/10.1145/3287560.3287596.
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