X-Risk Analysis for AI Research

• URL: http://arxiv.org/abs/2206.05862v7
• Date: Tue, 20 Sep 2022 16:49:56 GMT
• Title: X-Risk Analysis for AI Research
• Authors: Dan Hendrycks, Mantas Mazeika
• Abstract summary: We provide a guide for how to analyze AI x-risk. First, we review how systems can be made safer today. Next, we discuss strategies for having long-term impacts on the safety of future systems.
• Score: 24.78742908726579
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Artificial intelligence (AI) has the potential to greatly improve society, but as with any powerful technology, it comes with heightened risks and responsibilities. Current AI research lacks a systematic discussion of how to manage long-tail risks from AI systems, including speculative long-term risks. Keeping in mind the potential benefits of AI, there is some concern that building ever more intelligent and powerful AI systems could eventually result in systems that are more powerful than us; some say this is like playing with fire and speculate that this could create existential risks (x-risks). To add precision and ground these discussions, we provide a guide for how to analyze AI x-risk, which consists of three parts: First, we review how systems can be made safer today, drawing on time-tested concepts from hazard analysis and systems safety that have been designed to steer large processes in safer directions. Next, we discuss strategies for having long-term impacts on the safety of future systems. Finally, we discuss a crucial concept in making AI systems safer by improving the balance between safety and general capabilities. We hope this document and the presented concepts and tools serve as a useful guide for understanding how to analyze AI x-risk.

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