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.

翻译：人工智能是21世纪最具变革性的技术之一。未来AI能力的发展程度与范围仍是关键的不确定因素,各界对时间进程与潜在影响存在广泛分歧。随着各国与科技企业竞相提升AI系统的复杂性与自主性,对不透明AI决策过程的整合程度与监管力度引发担忧。这一现象在机器学习子领域尤为突出——当系统无需人工辅助即可自主优化目标时,目标可能被不完善地定义,或以预期外甚至危险的方式执行。随着系统能力与自主性的提升,突然的能力跃迁可能导致权力格局的意外变动甚至灾难性后果,使问题愈发严峻。本研究提出层级化复杂系统框架,用于建模AI风险并为替代性未来分析提供模板。通过收集公共与私营领域专家调查数据,对AI影响与可能性进行分类。结果显示,在强AI主体场景中不确定性增加,多智能体环境存在较高置信度,而AI对齐失败与影响力寻求行为的担忧持续加剧。