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

2026-06-25 · Source: Paper Index on ACL Anthology · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Advanced, quick

Summary

A new decision framework is presented for evaluating bias and fairness risks in Large Language Models (LLMs), emphasizing their variability across deployment contexts. This framework provides systematic guidance for selecting appropriate metrics by mapping LLM use cases, defined by a model and prompt population, to relevant bias and fairness measures. It considers task type, whether prompts contain protected attribute mentions, and stakeholder priorities. The framework addresses toxicity, stereotyping, counterfactual unfairness, and allocational harms, introducing novel metrics derived from stereotype classifiers and counterfactual adaptations of text similarity. An open-source Python library, "langfair", is released for practical adoption. Extensive experiments across five LLMs and five prompt populations demonstrate that fairness risks are highly context-dependent and cannot be reliably assessed solely from benchmark performance.

Key takeaway

For Machine Learning Engineers deploying Large Language Models, relying solely on general benchmarks for fairness assessment is insufficient and misleading. You must adopt a use-case-specific evaluation strategy, considering your model, prompt population, task type, and stakeholder priorities. Implement the proposed decision framework and utilize the "langfair" Python library to systematically select and apply appropriate bias and fairness metrics, ensuring robust and contextually relevant risk mitigation.

Key insights

LLM bias and fairness evaluation requires a use-case-specific approach, as risks vary significantly across deployment contexts.

Principles

• Bias risks vary by deployment context.
• Metric selection needs systematic guidance.
• Stakeholder priorities inform fairness.

Method

A decision framework maps LLM use cases, characterized by a model and prompt population, to relevant bias and fairness metrics based on task type, protected attribute mentions, and stakeholder priorities.

In practice

• Utilize the "langfair" Python library.
• Evaluate toxicity and stereotyping.
• Assess counterfactual unfairness.

Topics

• Large Language Models
• Bias Evaluation
• Fairness Metrics
• langfair
• Counterfactual Fairness
• Toxicity

Best for: Research Scientist, AI Architect, AI Engineer, AI Scientist, Machine Learning Engineer, AI Ethicist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.