Related papers: Gender Bias in Emotion Recognition by Large Language Models

Gender Bias in Emotion Recognition by Large Language Models

URL: http://arxiv.org/abs/2511.19785v1
Date: Mon, 24 Nov 2025 23:24:54 GMT
Title: Gender Bias in Emotion Recognition by Large Language Models
Authors: Maureen Herbert, Katie Sun, Angelica Lim, Yasaman Etesam,
Abstract summary: We investigate whether large language models (LLMs) exhibit gender biases when presented with a description of a person and their environment.<n>We propose and evaluate several debiasing strategies, demonstrating that achieving meaningful reductions in bias requires training based interventions.
Score: 1.515443880360935
License: http://creativecommons.org/licenses/by/4.0/
Abstract: The rapid advancement of large language models (LLMs) and their growing integration into daily life underscore the importance of evaluating and ensuring their fairness. In this work, we examine fairness within the domain of emotional theory of mind, investigating whether LLMs exhibit gender biases when presented with a description of a person and their environment and asked, "How does this person feel?". Furthermore, we propose and evaluate several debiasing strategies, demonstrating that achieving meaningful reductions in bias requires training based interventions rather than relying solely on inference-time prompt-based approaches such as prompt engineering.

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