Artificial Intelligence (AI) is one of the most transformative technologies
of the 21st century. The extent and scope of future AI capabilities remain a
key uncertainty, with widespread disagreement on timelines and potential
impacts. As nations and technology companies race toward greater complexity and
autonomy in AI systems, there are concerns over the extent of integration and
oversight of opaque AI decision processes. This is especially true in the
subfield of machine learning (ML), where systems learn to optimize objectives
without human assistance. Objectives can be imperfectly specified or executed
in an unexpected or potentially harmful way. This becomes more concerning as
systems increase in power and autonomy, where an abrupt capability jump could
result in unexpected shifts in power dynamics or even catastrophic failures.
This study presents a hierarchical complex systems framework to model AI risk
and provide a template for alternative futures analysis. Survey data were
collected from domain experts in the public and private sectors to classify AI
impact and likelihood. The results show increased uncertainty over the powerful
AI agent scenario, confidence in multiagent environments, and increased concern
over AI alignment failures and influence-seeking behavior.

本文使用分层复杂系统框架对人工智能（AI）风险进行建模，并从公共和私营领域的领域专家收集调查数据以分类 AI 影响和可能性，结果显示强大的 AI 代理情景有更多不确定性，对 AI 对齐失败和影响寻求行为的关注增加以及对多智能体环境的信心增强。