Computer Science > Artificial Intelligence
arXiv:2405.06624 (cs)
[Submitted on 10 May 2024 (v1), last revised 8 Jul 2024 (this version, v3)]
Title:Towards Guaranteed Safe AI: A Framework for Ensuring Robust and Reliable AI Systems
Authors:David "davidad" Dalrymple,Joar Skalse,Yoshua Bengio,Stuart Russell,Max Tegmark,Sanjit Seshia,Steve Omohundro,Christian Szegedy,Ben Goldhaber,Nora Ammann,Alessandro Abate,Joe Halpern,Clark Barrett,Ding Zhao,Tan Zhi-Xuan,Jeannette Wing,Joshua Tenenbaum
View a PDF of the paper titled Towards Guaranteed Safe AI: A Framework for Ensuring Robust and Reliable AI Systems, by David "davidad" Dalrymple and Joar Skalse and Yoshua Bengio and Stuart Russell and Max Tegmark and Sanjit Seshia and Steve Omohundro and Christian Szegedy and Ben Goldhaber and Nora Ammann and Alessandro Abate and Joe Halpern and Clark Barrett and Ding Zhao and Tan Zhi-Xuan and Jeannette Wing and Joshua Tenenbaum
View PDFHTML (experimental)Abstract:Ensuring that AI systems reliably and robustly avoid harmful or dangerous behaviours is a crucial challenge, especially for AI systems with a high degree of autonomy and general intelligence, or systems used in safety-critical contexts. In this paper, we will introduce and define a family of approaches to AI safety, which we will refer to as guaranteed safe (GS) AI. The core feature of these approaches is that they aim to produce AI systems which are equipped with high-assurance quantitative safety guarantees. This is achieved by the interplay of three core components: a world model (which provides a mathematical description of how the AI system affects the outside world), a safety specification (which is a mathematical description of what effects are acceptable), and a verifier (which provides an auditable proof certificate that the AI satisfies the safety specification relative to the world model). We outline a number of approaches for creating each of these three core components, describe the main technical challenges, and suggest a number of potential solutions to them. We also argue for the necessity of this approach to AI safety, and for the inadequacy of the main alternative approaches.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2405.06624 [cs.AI] |
| (orarXiv:2405.06624v3 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2405.06624 arXiv-issued DOI via DataCite |
Submission history
From: Joar Skalse [view email][v1] Fri, 10 May 2024 17:38:32 UTC (1,679 KB)
[v2] Fri, 17 May 2024 13:31:36 UTC (1,679 KB)
[v3] Mon, 8 Jul 2024 13:35:00 UTC (1,685 KB)
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View a PDF of the paper titled Towards Guaranteed Safe AI: A Framework for Ensuring Robust and Reliable AI Systems, by David "davidad" Dalrymple and Joar Skalse and Yoshua Bengio and Stuart Russell and Max Tegmark and Sanjit Seshia and Steve Omohundro and Christian Szegedy and Ben Goldhaber and Nora Ammann and Alessandro Abate and Joe Halpern and Clark Barrett and Ding Zhao and Tan Zhi-Xuan and Jeannette Wing and Joshua Tenenbaum
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