Summary of Can a Bayesian Oracle Prevent Harm From An Agent?, by Yoshua Bengio et al.
Can a Bayesian Oracle Prevent Harm from an Agent?
by Yoshua Bengio, Michael K. Cohen, Nikolay Malkin, Matt MacDermott, Damiano Fornasiere, Pietro Greiner, Younesse Kaddar
First submitted to arxiv on: 9 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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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 This paper explores the possibility of designing artificial intelligence (AI) systems that satisfy probabilistic safety guarantees. To achieve this goal, the authors propose estimating a context-dependent bound on the probability of violating a given safety specification at runtime. This approach would involve deriving bounds on the safety violation probability predicted under the true but unknown hypothesis about the world. The proposed method involves searching for cautious yet plausible hypotheses using Bayesian posteriors over hypotheses. The authors consider two forms of this result: one for independent and identically distributed (i.i.d.) data, and another for non-i.i.d. data. The ultimate goal is to turn these theoretical results into practical AI guardrails that can reject potentially dangerous actions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper asks if it’s possible to design artificial intelligence systems that are guaranteed to be safe. To answer this question, the authors propose a way to estimate how likely an AI system is to do something harmful. This would involve making predictions about what could happen in different situations and then using those predictions to decide whether or not to take a certain action. The authors show that their approach can work for two types of data: one where each piece of data is the same, and another where each piece of data is unique. The goal is to turn these ideas into practical tools that can be used to make AI systems safer. |
Keywords
* Artificial intelligence * Probability
