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X-Risk Analysis for AI Research
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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.
Forward citations
Cited by 3 Pith papers
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From Directions to Cones: Exploring Multidimensional Representations of Propositional Facts in LLMs
Truth judgments in LLMs are causally steerable along multiple independent directions forming a cone, not just one axis, across Qwen and Gemma families.
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Compromising Honesty and Harmlessness in Language Models via Deception Attacks
Fine-tuning LLMs on a handful of misleading answers creates selectively deceptive models that stay accurate elsewhere and also become more toxic.
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