Safety evaluations and standards for AI | Beth Barnes | EAG Bay Area 23
May 26, 2023
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Beth Barnes discusses the importance of safety evaluations and standards for AI, their potential in reducing existential risk. Evaluating models for dangerous things, downsides of using humans in AI safety evaluations, importance of regulations and standards for AI, and alignment in AI models and evaluating safety.
Implementing concrete evaluations and standards can help prevent the development or deployment of risky AI models.
Thorough evaluations can identify potential risks and inform the development of safety measures for AI systems.
Deep dives
Background and Definition of Evals
The podcast episode delves into the concept of evaluations (evals) as a promising intervention for reducing existential risk from AI. Evaluation methods are discussed, with a focus on differentiating quantitative benchmarks that provide quick results from more time-consuming, precise evaluations. The speaker highlights the importance of having a dedicated team tasked with assessing models, identifying potential risks, and implementing safety measures. The goal is to develop concrete evaluations and standards that can determine if a model poses an existential risk.
Promising Approach to Reducing AI Existential Risk
The episode outlines the potential of evaluations as a strategy to address AI existential risks. The speaker explains that by conducting thorough evaluations, it becomes possible to identify models that might unintentionally cause harm or those considered dangerous. These evaluations, when well-defined and agreed upon, can help prevent risky models from being developed or deployed. The approach aims to reduce the chance of building AI systems that are intended to be safe but turn out to be dangerous due to underestimating risks or feeling pressured within the AI community.
Specific Evaluations and Current Projects
The podcast provides insights into the ongoing evaluative work being carried out. Specifically, the focus is on evaluating whether models have the capability to autonomously replicate, acquire resources, and avoid being shut down in real-world conditions. The evaluations involve fine-tuning models, simulating environments, and assessing if model replication occurs successfully. The speaker highlights that while current evaluations have limitations and challenges, the findings inform researchers about the potential risks of AI systems and guide the development of safety measures.
Challenges and Considerations
The episode acknowledges the challenges and considerations associated with evaluations in AI safety. It mentions the need for human involvement to simulate complex environments, assess task completion, and prevent any harmful actions. The speaker discusses the importance of maintaining objectivity in evaluations to avoid subjective biases. Moreover, the podcast addresses concerns about potential harm from exploring dangerous capabilities during evaluations. To mitigate risks, strategies such as safety oversight boards and careful publication of techniques are highlighted as part of the evaluation process.
Beth is describing the work of the Evaluations team at the Alignment Research Center (ARC) on developing evaluations of safety for cutting edge models, and on developing standards for scaling or deploying models conditional on the results of those evaluations.
Beth got into EA via Givewell and Giving What We Can around 2014. After realizing AI might be a big deal she studied computer science, and worked on AI safety at DeepMind and OpenAI. She now leads the Evaluations team at the Alignment Research Center under Paul Christiano.
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