How True is GPT-2? An Empirical Analysis of Intersectional Occupational Biases

By Hannah Rose Kirk, Yennie Jun, Haider Iqbal, Elias Benussi, Filippo Volpin, Frédéric A. Dreyer, Aleksandar Shtedritski, Yuki M. Asano. 2021

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Training language models to follow instructions with human feedback (2022, background): “Language models can produce biased outputs (Dhamala et al., 2021; Liang et al., 2021; Manela et al., 2021; Caliskan et al., 2017; Kirk et al., 2021), leak private data (Carlini et al., 2021), generate misinformation (Solaiman et al., 2019; Buchanan et al., 2021), and be used maliciously; for a…”
Towards Intersectionality in Machine Learning: Including More Identities, Handling Underrepresentation, and Performing Evaluation (2022, background): “Algorithmic fairness methods have begun to consider intersectional attributes beyond just one axis of identity [81, 117, 118, 124].”
Gender and Representation Bias in GPT-3 Generated Stories (2021, background): “In previous work, GPT-3’s predecessor GPT-2 also places women in caregiving roles (Kirk et al., 2021), and character tropes for women emphasize maternalism and appearance (Gala et al., 2020).”