Summary of Declare and Justify: Explicit Assumptions in Ai Evaluations Are Necessary For Effective Regulation, by Peter Barnett et al.
Declare and Justify: Explicit assumptions in AI evaluations are necessary for effective regulation
by Peter Barnett, Lisa Thiergart
First submitted to arxiv on: 19 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computers and Society (cs.CY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a regulatory framework for ensuring the safety of AI systems by requiring developers to justify key underlying assumptions about evaluations. The authors identify three core assumptions: comprehensive threat modeling, proxy task validity, and adequate capability elicitation. They argue that these assumptions cannot currently be well justified and that regulation should require AI development to halt if evaluations demonstrate unacceptable danger or if these assumptions are inadequately justified. The approach aims to enhance transparency in AI development and provide a practical path towards more effective governance of advanced AI systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AI researchers want to make sure that AI systems are safe before they’re used. To do this, they think we should require developers to explain why they think their AI is safe. They found three important things that need to be justified: making sure all the bad things that could happen are thought of, checking if tasks used to test the AI are good ones, and making sure the AI can really do what it’s supposed to do. If these justifications aren’t good enough or if the AI is too dangerous, then development should stop. This approach will make AI development more transparent and help us govern advanced AI systems better. |
