Year: 2025
Type: conference-paper
Abstract: Current approaches to AI safety predominantly focus on specifying correct behavior through software, data, and rules. This work argues that this approach faces theoretically fundamental, and not merely practical, limitations. I present a multi-layered analysis of this paradigm, demonstrating its inherent barriers from the perspectives of computational complexity, information theory, and physical engineering. In ongoing work, I prove that even simplified forms of semantic self-verification are computationally intractable (NP-complete). I use information theory to show that any specification of an external, ambiguous concept like "harm" is necessarily incomplete. To address these limits, I develop a framework for reasoning about verifiable, physically-enforced safety bounds that are independent of software state.
Author: Robin Young
Institution: University of Cambridge
Language: English
Subfield: Safety Research
Field: Social Sciences
Domain: Social Sciences
Open Access status: bronze
OpenAlex ID: W4415195147
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