Measuring Harmful Sentence Completion in Language Models for LGBTQIA+ Individuals

By Debora Nozza, Federico Bianchi, Anne Lauscher, Dirk Hovy. 2022

73 citations recorded by the source corpus.

Current language technology is ubiquitous and directly influences individuals’ lives worldwide. Given the recent trend in AI on training and constantly releasing new and powerful large language models (LLMs), there is a need to assess their biases and potential concrete consequences. While some studies have highlighted the shortcomings of these models, there is only little on the negative impact of LLMs on LGBTQIA+ individuals. In this paper, we investigated a state-of-the-art template-based approach for measuring the harmfulness of English LLMs sentence completion when the subjects belong to the LGBTQIA+ community. Our findings show that, on average, the most likely LLM-generated completion is an identity attack 13% of the time. Our results raise serious concerns about the applicability of these models in production environments.

Subfields: Artificial Intelligence, Safety, Robustness & Interpretability

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Citations in the corpus

Guiding the Release of Safer E2E Conversational AI through Value Sensitive Design (2022, background): “However, these classifiers themselves can have issues with bias, e.g., by learning undesirable correlations that tie toxicity to identity terms (Dixon et al., 2018; Nozza et al., 2021, 2022), or language varieties, such as African American English (Liu et al., 2019; Sap et al., 2019).”
“I’m fully who I am”: Towards Centering Transgender and Non-Binary Voices to Measure Biases in Open Language Generation (2023, background): “, [43, 60, 63]) have focused on understanding how LLM harms appear for the TGNB community.”