An Actionable Framework for Assessing Bias and Fairness in Large Language Model Use Cases

2024-07-15

arXiv.org

AI Summary

This paper addresses fairness concerns—such as gender, racial, sexual orientation, and age bias—in large language models (LLMs). It introduces the first fairness evaluation framework that treats an “LLM use case” (i.e., a specific model paired with a prompt set) as the fundamental unit of analysis. Methodologically, it establishes a structured risk taxonomy covering diverse bias scenarios and maps each to lightweight, parameter-free metrics relying solely on model outputs. The open-source toolkit LangFair enables end-to-end, automated fairness auditing. Experimental results reveal substantial variation in bias severity across use cases. The framework has been validated in multiple real-world deployments, demonstrating prompt-specificity, model-specificity, and engineering practicality.

Technology Category

• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Machine Learning: Large Multimodal Models (LMMs)
• Philosophy and Ethics of AI: Bias, Fairness & Equity

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• Search and Retrieval-Augmented AI: Large language models for search

Abstract

Large language models (LLMs) can exhibit bias in a variety of ways. Such biases can create or exacerbate unfair outcomes for certain groups within a protected attribute, including, but not limited to sex, race, sexual orientation, or age. In this paper, we propose a decision framework that allows practitioners to determine which bias and fairness metrics to use for a specific LLM use case. To establish the framework, we define bias and fairness risks for LLMs, map those risks to a taxonomy of LLM use cases, and then define various metrics to assess each type of risk. Instead of focusing solely on the model itself, we account for both prompt-specific- and model-specific-risk by defining evaluations at the level of an LLM use case, characterized by a model and a population of prompts. Furthermore, because all of the evaluation metrics are calculated solely using the LLM output, our proposed framework is highly practical and easily actionable for practitioners. For streamlined implementation, all evaluation metrics included in the framework are offered in this paper's companion Python toolkit, LangFair. Finally, our experiments demonstrate substantial variation in bias and fairness across use cases, underscoring the importance of use-case-level assessments.

Problem

• Framework for bias assessment
• Fairness metrics in LLMs
• Use-case-level risk evaluation

Innovation

• Bias and fairness assessment framework
• Use-case-specific evaluation metrics
• Python toolkit for streamlined implementation