Fairness and Bias in LLM Guardrail Moderation

Investigate fairness and bias in moderation decisions produced by large language model guardrail systems, including the OpenGuardrails platform and its unified detector (OpenGuardrails-Text-2510), and establish continuous evaluation and calibration procedures to address these open challenges across languages, categories, and deployment contexts.

Background

The paper presents OpenGuardrails, an open-source, unified LLM-based guardrails system for content safety, model manipulation detection, and data leakage prevention. While the system achieves strong multilingual performance and configurable policy control, the authors explicitly acknowledge that fairness and bias in moderation decisions remain unresolved issues.

This concern applies broadly to moderation systems and is particularly relevant to OpenGuardrails given its multilingual coverage and dynamic sensitivity thresholding. Ensuring equitable and unbiased moderation across heterogeneous languages, categories, and enterprise policies requires ongoing assessment and calibration, which the authors identify as an open challenge.