Toward a Theory of Value in AI Alignment 文章

ArXiv CS.AI2026-08-12PAPERen作者: Andrew Smart, Shazeda Ahmed, Jackie Kay, Jimmy Tobin, Kris Shrishak, Abeba Birhane

详细信息

来源站点
ArXiv CS.AI
作者
Andrew Smart, Shazeda Ahmed, Jackie Kay, Jimmy Tobin, Kris Shrishak, Abeba Birhane
文章类型
PAPER
语言
en
发布日期
2026-08-12

摘要

arXiv:2608.10327v1 Announce Type: new Abstract: Can AI systems be aligned to human values? The popularization of large language models (LLMs) and multi-modal foundation models has seen a rise in harms spanning from toxic speech and hallucinations to AI agents executing unauthorized actions. Within the field of AI safety, these harmful instances are often framed as the alignment problem, or of models being misaligned with human values. Researchers have responded by pursuing applied and theoretical AI value alignment efforts, often without specifying what they mean by human values. How does the field of AI value alignment conceive of human values? How are these conceptions of values technically operationalized and evaluated? What does the emergent theory of value from this field signify for the future of AI? We annotated 94 value alignment research papers to discern their implicit theory of values in AI.