The field of AI safety research is expanding rapidly, but researchers often struggle to get timely, high-quality feedback on their ideas. While conferences and personal networks provide some opportunities for validation, these are limited and infrequent. This creates bottlenecks in the important process of refining and improving AI safety concepts.

A Feedback Platform for AI Safety Ideas

One potential solution involves creating a specialized platform where researchers could submit their AI safety ideas to receive automated yet thoughtful feedback. When a researcher submits an idea description, a language model trained on AI safety papers could analyze it and generate constructive responses. The feedback might highlight strengths and weaknesses, suggest relevant existing research, offer ideas for further development, or point out potential risks.

The system could benefit various stakeholders:

• Independent researchers who lack institutional connections
• Newcomers needing guidance on their ideas
• Established researchers wanting quick preliminary feedback
• Organizations looking to evaluate incoming proposals

Implementation Possibilities

A simple starting version might include just a web interface connected to existing language models. More advanced versions could involve custom training on safety literature and optional human verification. To address concerns, the platform might offer anonymous submissions and clear policies about intellectual property.

The concept differs from general research tools by specifically focusing on the ideation phase of AI safety work. Where services like Elicit help find papers, this would suggest how to strengthen emerging ideas. Unlike human-only feedback systems, it could provide near-instant responses while still allowing for human oversight when needed.

Potential Path Forward

Testing key assumptions would be important before full development - verifying that models can actually give useful safety feedback and that researchers would use such a system. An MVP could assess feasibility while more advanced versions might explore hybrid human-AI approaches or integration with research workflows. Various funding models could support development while keeping the focus on advancing AI safety research.

This approach attempts to balance the scale of automation with the nuance needed for safety discussions, potentially accelerating progress in this crucial field.