An Actionable Framework for Assessing Bias and Fairness in Large Language Model Use Cases
Dylan Bouchard
TL;DR
This work tackles the challenge of assessing bias and fairness in LLMs by shifting the focus from model-level benchmarks to use-case–level evaluations guided by a formal taxonomy of prompts and protected attributes. It introduces an actionable framework that maps three task categories—text generation/summarization, classification, and recommendation—to a curated set of output-only metrics, including novel counterfactual and stereotype-based measures, all implemented in the LangFair toolkit. The framework accommodates prompt-specific risks (FTU considerations) and stakeholder preferences, enabling practitioners to tailor metric selections while maintaining practical feasibility. Experiments across six use cases reveal substantial variation in bias and fairness across use cases, underscoring the importance of use-case–level assessments and providing a pragmatic pathway for industry deployment of bias and fairness evaluations in LLM applications.
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.