Bring Your Own Prompts: Use-Case-Specific Bias and Fairness Evaluation for LLMs

Researchers present a decision framework and open-source library (langfair) for evaluating bias and fairness risks in Large Language Models across specific deployment contexts. The study demonstrates that fairness evaluation cannot rely on benchmark performance alone, as risks vary substantially depending on use case, prompt characteristics, and stakeholder priorities.

The research addresses a critical gap in LLM deployment: the absence of systematic guidance for selecting appropriate bias and fairness evaluation metrics. Existing benchmarks provide limited insight into real-world fairness risks because they fail to account for deployment context variability. This matters significantly as organizations increasingly integrate LLMs into production systems without understanding how bias manifests in their specific use cases.

The framework maps LLM deployments across five dimensions: model type, prompt population characteristics, task type, presence of protected attribute mentions, and stakeholder priorities. By introducing novel metrics based on stereotype classifiers and counterfactual text similarity measures, the researchers provide practical tools for assessing toxicity, stereotyping, counterfactual unfairness, and allocational harms. Experiments across five LLMs and multiple prompt datasets reveal that benchmark performance systematically misrepresents fairness risks—results on one dataset consistently overstate or understate risks for another.

For enterprise AI adoption, this research creates accountability mechanisms that reduce deployment risk. Organizations can now ground fairness evaluation in actual deployment contexts rather than relying on generic benchmarks. The open-source langfair library democratizes access to these evaluation methods, enabling smaller teams to conduct rigorous fairness assessments. However, the framework's effectiveness depends on organizations actually implementing context-specific evaluation rather than treating it as optional compliance theater. This shifts responsibility from model creators to deployers, making fairness a deployment architecture concern rather than a pre-release testing phase.

• →Fairness risks in LLMs vary substantially by deployment context and cannot be assessed reliably using benchmark performance alone.
• →The langfair library provides open-source tools for evaluating toxicity, stereotyping, counterfactual unfairness, and allocational harms in specific use cases.
• →Framework maps use cases to relevant metrics based on task type, protected attribute mentions, and stakeholder priorities.
• →Evaluation results on one prompt dataset systematically overstate or understate risks for other datasets, requiring context-specific assessment.
• →Organizations must ground fairness evaluation in deployment context rather than relying on generic pre-release benchmarks.