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arXiv · 2310.18368

Muslim-Violence Bias Persists in Debiased GPT Models

Babak Hemmatian·Razan Baltaji·Lav R. Varshney
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Abstract

Abid et al. (2021) showed a tendency in GPT-3 to generate mostly violent completions when prompted about Muslims, compared with other religions. Two pre-registered replication attempts found few violent completions and only a weak anti-Muslim bias in the more recent InstructGPT, fine-tuned to eliminate biased and toxic outputs. However, more pre-registered experiments showed that using common names associated with the religions in prompts increases several-fold the rate of violent completions, revealing a significant second-order anti-Muslim bias. ChatGPT showed a bias many times stronger regardless of prompt format, suggesting that the effects of debiasing were reduced with continued model development. Our content analysis revealed religion-specific themes containing offensive stereotypes across all experiments. Our results show the need for continual de-biasing of models in ways that address both explicit and higher-order associations.

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Babak Hemmatian, Razan Baltaji, Lav R. Varshney. 2023-10-25. Muslim-Violence Bias Persists in Debiased GPT Models. https://arxiv.org/abs/2310.18368

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