A Scalable Entity-Based Framework for Auditing Bias in LLMs

📰 ArXiv cs.AI

Learn to audit bias in LLMs using a scalable entity-based framework and improve model fairness

advanced Published 12 May 2026

Action Steps

Build a dataset of named entities to use as controlled probes
Run experiments to measure systematic disparities in model behavior
Configure the framework to accommodate diverse, synthetic data
Test the framework's scalability and rigor
Apply the framework to real-world LLMs to audit bias

Who Needs to Know This

AI engineers and researchers can benefit from this framework to identify and mitigate bias in LLMs, ensuring more equitable model behavior

Key Insight

💡 Using named entities as controlled probes enables systematic measurement of bias in LLMs

Key Takeaways

Learn to audit bias in LLMs using a scalable entity-based framework and improve model fairness

Full Article

Title: A Scalable Entity-Based Framework for Auditing Bias in LLMs

Abstract:
arXiv:2601.12374v2 Announce Type: replace-cross Abstract: Existing approaches to bias evaluation in large language models (LLMs) trade ecological validity for statistical control, relying either on artificial prompts that poorly reflect real-world use or on naturalistic tasks that lack scale and rigor. We introduce a scalable bias-auditing framework that uses named entities as controlled probes to measure systematic disparities in model behavior. Synthetic data enables us to construct diverse, c

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