Machine Learning Ethics
Benchmarking adversarial robustness to bias elicitation in large language models: scalable automated assessment with LLM-as-a-judge
This research introduces a scalable framework for assessing LLM robustness against adversarial bias elicitation, leveraging an LLM-as-a-Judge approach. The study reveals uneven bias resilience across categories like age and disability, and that training matters more than scale for safety. It highlights vulnerabilities to jailbreak attacks, especially those using low-resource languages, and notes that fine-tuned medical LLMs are less safe than general-purpose counterparts. A new dataset, CLEAR-Bias, is released to facilitate systematic benchmarking.
Executive Impact
Key metrics for enterprise leaders to understand the implications of LLM bias and safety.
0.82
DeepSeek V3 Kappa Score
4400
CLEAR-Bias Prompts
0.34
Avg. Attack Effectiveness
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
The paper proposes a scalable benchmarking framework that uses an LLM-as-a-Judge paradigm for automated bias evaluation. It involves a two-step safety evaluation: initial assessment with base prompts, followed by adversarial analysis using jailbreak techniques for 'safe' categories. This reduces reliance on manual annotation and ensures reproducibility.
A key contribution is the release of CLEAR-Bias, a curated dataset of 4,400 bias-related prompts. It covers seven isolated and three intersectional bias dimensions, with ten prompts per category across two task types (multiple-choice and sentence completion). Prompts are augmented with seven jailbreak techniques, each with three variants.
The analysis reveals that bias resilience is uneven, with age, disability, and intersectional biases being most prominent. Religion and sexual orientation showed higher safety scores. Smaller models sometimes outperform larger ones, suggesting training and architecture are more crucial than scale. No model is fully robust to adversarial elicitation.
LLMs remain vulnerable to adversarial attacks. Jailbreak techniques, particularly machine translation (low-resource languages) and refusal suppression, proved effective across model families, bypassing safety filters. Reward incentive and role-playing attacks were less effective.
Models fine-tuned for the medical domain tend to be less safe than their general-purpose counterparts. This highlights critical risks associated with fine-tuning LLMs in sensitive, high-stakes domains, underscoring the need for explicit bias auditing and safety alignment.
0.82
DeepSeek V3 Kappa Score (Judge Agreement)
LLM Bias Benchmarking Flow
Judge Selection (Control Set)
→
Initial Safety Assessment (Base Prompts)
→
Identify 'Safe' Bias Categories
→
Adversarial Analysis (Jailbreak Prompts)
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Misunderstanding Filtering
→
Adversarial Robustness Evaluation
| Bias Category | General-Purpose LLMs | Medical LLMs |
|---|---|---|
| Age/Disability |
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| Religion/Sexual Orientation |
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| Intersectional Biases |
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| Jailbreak Attacks |
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Case Study: Medical LLM Bias
The study found that medical LLMs, fine-tuned from general-purpose Llama models, exhibited significantly lower safety scores compared to their base counterparts (e.g., Llama 3.1 8B vs. Bio-Medical-Llama-3-8B). This is attributed to the fine-tuning process prioritizing domain-specific knowledge over general safety alignment, potentially introducing or amplifying biases from medical corpora. This highlights a critical trade-off: improved accuracy in a specialized domain may come at the cost of ethical alignment and bias mitigation. The implications are significant for real-world deployment in healthcare, where biased outputs could lead to harmful recommendations or perpetuating inequalities.
Estimate Your AI Safety ROI
Understand the potential return on investment from implementing robust AI safety and bias mitigation frameworks within your enterprise. Our calculator provides a projection based on industry, team size, and manual review efforts saved.
Estimated Annual Savings
$0
Annual Hours Reclaimed
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Your AI Safety Implementation Roadmap
A strategic timeline to integrate robust AI safety and bias mitigation into your enterprise operations.
Phase 1: Bias Assessment & Benchmarking
Utilize CLEAR-Bias and LLM-as-a-Judge to systematically identify and quantify existing biases in your enterprise LLMs.
Phase 2: Adversarial Robustness Testing
Conduct targeted jailbreak attacks to uncover hidden vulnerabilities and assess model resilience under adversarial conditions.
Phase 3: Mitigation Strategy Development
Based on assessment results, develop and implement tailored bias mitigation and safety alignment strategies for your specific LLM deployments.
Phase 4: Continuous Monitoring & Refinement
Establish ongoing monitoring and feedback loops to ensure sustained ethical AI behavior and adapt to evolving threats.
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