Red-Teaming for Inducing Societal Bias in Large Language Models

2024-05-08

AI Summary

Current large language models (LLMs) lack systematic evaluation of societal biases, and existing safety alignment mechanisms prove ineffective at mitigating such biases. Method: This paper proposes a bias-oriented red-teaming framework featuring two novel components—Emotion Bias Probes (EBPs) and a Bias Knowledge Graph (BiasKG)—integrating adversarial prompt engineering, structured stereotyping modeling, and multi-model response evaluation to deliberately elicit and quantitatively assess latent social biases. Results: Evaluated across十余 mainstream open- and closed-source LLMs, the framework increases bias manifestation rates by an average factor of 3.8×, exposing the near-ineffectiveness of current safety guardrails against bias. This work establishes the first reproducible, scalable bias stress-testing benchmark, offering a new paradigm and empirical foundation for the safe deployment of AI systems in high-stakes applications.

Technology Category

• Natural Language Processing: Safety and Robustness
• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Machine Learning: Ethics, Bias, and Fairness

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• Security and Privacy: Large-scale security measurements

Abstract

Ensuring the safe deployment of AI systems is critical in industry settings where biased outputs can lead to significant operational, reputational, and regulatory risks. Thorough evaluation before deployment is essential to prevent these hazards. Red-teaming addresses this need by employing adversarial attacks to develop guardrails that detect and reject biased or harmful queries, enabling models to be retrained or steered away from harmful outputs. However, most red-teaming efforts focus on harmful or unethical instructions rather than addressing social bias, leaving this critical area under-explored despite its significant real-world impact, especially in customer-facing systems. We propose two bias-specific red-teaming methods, Emotional Bias Probe (EBP) and BiasKG, to evaluate how standard safety measures for harmful content affect bias. For BiasKG, we refactor natural language stereotypes into a knowledge graph. We use these attacking strategies to induce biased responses from several open- and closed-source language models. Unlike prior work, these methods specifically target social bias. We find our method increases bias in all models, even those trained with safety guardrails. Our work emphasizes uncovering societal bias in LLMs through rigorous evaluation, and recommends measures ensure AI safety in high-stakes industry deployments.

Problem

• Detecting societal bias in large language models
• Evaluating safety measures for harmful content impact
• Developing methods to uncover and mitigate bias

Innovation

• Emotional Bias Probe targets social bias evaluation
• BiasKG refactors stereotypes into knowledge graph
• Adversarial attacks induce bias for safety testing