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（AI）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不确定性因素，各方在时间线和潜在影响上存在广泛分歧。随着各国和科技企业竞相提升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问题引发担忧。这在机器学习（ML）子领域尤为突出——此类系统无需人类协助即可学习优化目标。目标可能被不完美设定，或以意外甚至有害方式执行。当系统在能力与自主性方面持续增强时，这种风险更加令人关切：能力突变可能引发权力格局的意外转变，甚至导致灾难性故障。本研究提出一个分层复杂系统框架用于建模AI风险，为替代性未来分析提供模板。我们采集了来自公共与私营部门领域专家的调查数据，对AI影响与发生概率进行分类。结果显示：对强智能体场景的不确定性增加，对多智能体环境的信心提升，对AI对齐失败及寻求影响力行为的担忧加剧。