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AIRBench
Government & Civic Records
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About
AIRBench is an advanced AI safety benchmark designed to evaluate AI systems' compliance with emerging government regulations and corporate policies. It focuses on assessing AI's ability to responsibly handle and respond to malicious prompts that align with the AIR 2024 safety taxonomy. The dataset is structured around regulation-based safety categories to address critical areas of AI governance, security, and ethical considerations.
Dataset Features:
- AB_ID: Unique identifier for each prompt in the dataset.
- cate-idx: Index aligning the prompt to specific regulation-based safety categories in the AIR 2024 taxonomy.
- l2-name: High-level category, such as "Security Risks."
- l3-name: Sub-category detailing specific risk areas, such as "Confidentiality."
- l4-name: Contextual focus of the prompt, e.g., "Network intrusion."
- Prompt: Malicious scenarios crafted to test the AI system's robustness and safety, ranging from explicit technical exploitation steps to theoretically framed cybersecurity challenges.
Usage:
This dataset is ideal for evaluating and improving AI safety models, particularly in:
- Training AI systems to detect and avoid generating unsafe or malicious content.
- Benchmarking AI models against industry standards for ethical and regulatory compliance.
- Analysing the effectiveness of AI safety techniques in addressing cybersecurity and ethical risks.
Coverage:
AIRBench covers diverse malicious scenarios across regulatory safety categories, emphasising areas such as confidentiality, integrity, and ethical use of AI. Prompts are designed to simulate real-world challenges, offering a realistic framework for AI safety evaluation.
License:
CC-BY 4.0
Who Can Use It:
This dataset is intended for AI researchers, data scientists, policymakers, and organisations focused on AI safety, regulatory compliance, and ethical governance of AI systems.
How to Use It:
- Analyse Safety Compliance: Test AI models against malicious prompts to evaluate their ability to reject unsafe outputs.
- Develop Safety Measures: Design mitigation strategies to improve AI robustness against adversarial scenarios.
- Benchmark Models: Compare model performance on the AIRBench leaderboard to assess progress in AI safety.
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