Web: http://arxiv.org/abs/2209.06899

Sept. 16, 2022, 1:16 a.m. | Lisa P. Argyle, Ethan C. Busby, Nancy Fulda, Joshua Gubler, Christopher Rytting, David Wingate

cs.CL updates on arXiv.org arxiv.org

We propose and explore the possibility that language models can be studied as
effective proxies for specific human sub-populations in social science
research. Practical and research applications of artificial intelligence tools
have sometimes been limited by problematic biases (such as racism or sexism),
which are often treated as uniform properties of the models. We show that the
"algorithmic bias" within one such tool -- the GPT-3 language model -- is
instead both fine-grained and demographically correlated, meaning that proper
conditioning …

arxiv human language language models

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