The ALI study suggests that solutions to these problems may require more involvement from public-sector bodies, including clear articulation of what is expected from evaluations and transparency about their limitations. The study also recommends the development of "context-specific" evaluations that consider the potential impact on different types of users and potential attacks on models. However, the study concludes that while evaluations can identify potential risks, they cannot guarantee a model's safety.
Key takeaways:
- Current tests and benchmarks for AI safety and accountability may be inadequate, according to a report by the Ada Lovelace Institute (ALI).
- The report found that while current evaluations can be useful, they can be easily manipulated, don't necessarily reflect how models will behave in real-world scenarios, and may not be exhaustive.
- There is a lack of agreed-upon standards for 'red-teaming', the practice of identifying vulnerabilities and flaws in a model, making it difficult to assess the effectiveness of such efforts.
- Experts suggest that regulators and policymakers must clearly articulate what they want from evaluations, and that it may be necessary to develop context-specific evaluations that consider the types of users a model might impact and the ways in which attacks on models could defeat safeguards.