Bias in Language Models: Beyond Trick Tests and Toward RUTEd Evaluation

2024-02-20

arXiv.org

AI Summary

Existing LLM bias benchmarks—such as gender-occupation association tests—lack ecological validity and fail to reflect bias manifestations in realistic long-text generation tasks, suffering from a critical gap in “Real-Use and Real-Impact Evaluation” (RUTEd). Method: The authors first conduct a systematic empirical study, finding no statistically significant correlation (p > 0.05) between standard bias metrics and bias observed across three real-world tasks: children’s story generation, user persona modeling, and English-learning content generation. They then propose the RUTEd evaluation paradigm, redesigning neutrality, skewness, and stereotypicality metrics to be context-sensitive and outcome-quantifiable. Contribution/Results: Experiments reveal that models selected as “fairest” under conventional benchmarks perform no better than random selection in actual deployment scenarios. This work shifts bias evaluation from artificial, static stereotype traps toward an embodied, situated, and impact-measurable framework grounded in authentic usage contexts.

Technology Category

• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Machine Learning: Ethics, Bias, and Fairness
• Philosophy and Ethics of AI: Bias, Fairness & Equity

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Social Networks and Social Media: Fairness and bias in social network and social media analysis

Abstract

Standard benchmarks of bias and fairness in large language models (LLMs) measure the association between social attributes implied in user prompts and short LLM responses. In the commonly studied domain of gender-occupation bias, we test whether these benchmarks are robust to lengthening the LLM responses as a measure of Realistic Use and Tangible Effects (i.e., RUTEd evaluations). From the current literature, we adapt three standard bias metrics (neutrality, skew, and stereotype), and we develop analogous RUTEd evaluations from three contexts of real-world use: children's bedtime stories, user personas, and English language learning exercises. We find that standard bias metrics have no significant correlation with the more realistic bias metrics. For example, selecting the least biased model based on the standard"trick tests"coincides with selecting the least biased model as measured in more realistic use no more than random chance. We suggest that there is not yet evidence to justify standard benchmarks as reliable proxies of real-world biases, and we encourage further development of context-specific RUTEd evaluations.

Problem

• Evaluate bias in LLMs beyond short responses
• Develop realistic metrics for real-world LLM use
• Assess correlation between standard and realistic bias measures

Innovation

• Lengthening LLM responses
• Adapting standard bias metrics
• Developing RUTEd evaluations