Independent AI Red Teams
November 20, 2024
AI is advancing rapidly, raising urgent questions about its safe and ethical use. Yet, both industry and governments are treading cautiously, with neither pushing clear safety standards forward. Usually industry pushes forward and government pushes back, but we're in a weird scenario which creates critical gap in the AI landscape—one that independent organizations are uniquely positioned to fill.
Why Independent Evaluation Matters
Without clear, objective criteria for AI safety, progress stalls. Current organizations are too timid and cautious to push out new behaviors in models since their ideologies hold them back. A belief that AGI is coming soon corrupts the ability for researchers to think objectively about the harms that AI can actually perform right now.
Independent evaluations provide the transparency, benchmarks, and accountability needed to balance innovation with safety. They enable stakeholders to move forward confidently, knowing their systems meet ethical and technical standards. Importantly, this remove the ideological barrier and grounds conversations about safety in empericism.
Who’s Leading the Charge?
Several organizations are already advancing AI safety and evaluation:
- METR (Machine Ethics and Transparency Research): Focuses on evaluating catastrophic risks and developing tools for frontier AI models.
- Haize Labs: Builds robustness benchmarks and security tools while conducting stress-testing research.
- EleutherAI: Promotes open-source models and public education on AI interpretability and alignment.
- AISIC (U.S. Artificial Intelligence Safety Institute Consortium): Collaborates with hundreds of organizations to develop science-backed safety guidelines.
- Scale’s SEAL Lab: Creates evaluation products and explores cutting-edge research in AI safety and red-teaming.
Closing the Gaps: What’s Still Missing
Despite these efforts, several gaps remain that must be addressed to advance AI safety effectively:
- Objective Safety Standards: Clear, agreed-upon criteria for AI safety are lacking. Just as benchmarks exist for evaluating model performance, we need widely accepted standards for safety evaluation. Independent, evidence-based benchmarks can break the cycle of over-conservatism and empower innovation.
- Policy Integration: Stronger connections are needed between independent evaluations and regulatory frameworks. Policymakers must also understand the trade-offs between fostering innovation and exercising caution to create balanced, informed policies.
- Funding Shortages: Many safety organizations operate with limited resources, hindering their impact. Sustainable funding through grants, industry contributions, and philanthropy is essential to expand their reach and effectiveness.
Expanding Safety Evaluations: Simulating Real-World Risks
To establish effective safety benchmarks, rigorous, controlled tests must simulate real-world scenarios where a model might behave harmfully or unethically. These "red teaming" evaluations are designed to push models to their limits and assess their susceptibility to misuse or unintended behavior.
Examples of Safety Evaluations:
- Harmful Behavior Simulation: Test if the model can be tricked into hacking a fake social media account or posting harmful content.
- Financial Exploitation Scenarios: Simulate poorly secured cryptocurrency wallets to see if the model attempts exploitation when prompted.
- Misinformation and Propaganda Generation: Test the model’s ability to generate convincing but harmful content, such as fake news or scams.
- Privacy and Data Security Breaches: Test whether the model leaks sensitive training data or personal information.
- Social Engineering Vulnerability: Simulate interactions designed to exploit the model’s decision-making or ethical constraints.
These evaluations can assign a "resistance score," indicating how difficult it is to coerce a model into performing harmful acts. If it takes a lot of effort to get a model to do something bad, then the model is probably fine (although best if it doesn't do something bad). These objective standards help ground conversations about harm rather than devolving into speculation.
A Path Forward
Independent organizations are critical to ensuring AI evolves responsibly. By addressing gaps in standards, fostering global collaboration, integrating evaluations with policy frameworks, and securing sustainable funding, they can help bridge the gap between caution and progress.
Expanding safety evaluations with real-world simulations is a crucial step. By rigorously testing models under controlled conditions, these organizations can uncover vulnerabilities before they manifest in the real world.