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

• URL: http://arxiv.org/abs/2102.04130v1
• Date: Mon, 8 Feb 2021 11:10:27 GMT
• Title: How True is GPT-2? An Empirical Analysis of Intersectional Occupational Biases
• Authors: Hannah Kirk, Yennie Jun, Haider Iqbal, Elias Benussi, Filippo Volpin, Frederic A. Dreyer, Aleksandar Shtedritski, Yuki M. Asano
• Abstract summary: Downstream applications are at risk of inheriting biases contained in natural language models. We analyze the occupational biases of a popular generative language model, GPT-2. For a given job, GPT-2 reflects the societal skew of gender and ethnicity in the US, and in some cases, pulls the distribution towards gender parity.
• Score: 50.591267188664666
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The capabilities of natural language models trained on large-scale data have increased immensely over the past few years. Downstream applications are at risk of inheriting biases contained in these models, with potential negative consequences especially for marginalized groups. In this paper, we analyze the occupational biases of a popular generative language model, GPT-2, intersecting gender with five protected categories: religion, sexuality, ethnicity, political affiliation, and name origin. Using a novel data collection pipeline we collect 396k sentence completions of GPT-2 and find: (i) The machine-predicted jobs are less diverse and more stereotypical for women than for men, especially for intersections; (ii) Fitting 262 logistic models shows intersectional interactions to be highly relevant for occupational associations; (iii) For a given job, GPT-2 reflects the societal skew of gender and ethnicity in the US, and in some cases, pulls the distribution towards gender parity, raising the normative question of what language models _should_ learn.

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