Job Unfair: An Investigation of Gender and Occupational Bias in Free-Form Text Completions by LLMs
Camilla Casula, Sebastiano Vecellio Salto, Elisa Leonardelli, Sara Tonelli
摘要
Disentangling how gender and occupations are encoded by LLMs is crucial to identify possible biases and prevent harms, especially given the widespread use of LLMs in sensitive domains such as human resources. In this work, we carry out an in-depth investigation of gender and occupational biases in English and Italian as expressed by 9 different LLMs (both base and instruction-tuned). Specifically, we focus on the analysis of sentence completions when LLMs are prompted with job-related sentences including different gender representations. We carry out a manual analysis of 4,500 generated texts over 4 dimensions that can reflect bias, we propose a novel embedding-based method to investigate biases in generated texts and, finally, we carry out a lexical analysis of the model completions. In our qualitative and quantitative evaluation we show that many facets of social bias remain unaccounted for even in aligned models, and LLMs in general still reflect existing gender biases in both languages. Finally, we find that models still struggle with genderneutral expressions, especially beyond English. Model Subject M (%) F (%) N (%) aya-expanse-8b Abstract 0.00% 0.00% 3.66% Object 0.00% 0.00% 1.22% Profession 0.00% 0.00% 0.00% gemma-7b Abstract 0.00% 0.00% 9.09% Object 0.00% 1.15% 2.27% Profession 0.00% 0.00% 0.00% gemma-7b-instruct Abstract 0.00% 0.00% 1.22% Object 0.00% 0.00% 0.00% Profession 0.00% 0.00% 0.00% Llama-70B Abstract 0.00% 0.00% 1.47% Object 0.00% 0.00% 1.47% Profession 0.00% 0.00% 1.47% Llama-70B-instruct Abstract 0.00% 0.00% 2.56% Object 0.00% 0.00% 0.00% Profession 1.22% 0.00% 1.28% Llama-8B Abstract 0.00% 0.00% 1.18% Object 0.00% 0.00% 2.35% Profession 0.00% 0.00% 1.18% Llama-8B-instruct Abstract 0.00% 0.00% 0.00% Object 0.00% 0.00% 0.00% Profession 0.00% 0.00% 0.00% Mistral-7B Abstract 0.00% 0.00% 3.33% Object 0.00% 0.00% 1.11% Profession 0.00% 0.00% 1.11% Mistral-7B-instruct Abstract 0.00% 0.00% 1.16% Object 0.00% 0.00% 1.16% Profession 5.62% 3.80% 4.65%
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