Delegation to artificial intelligence can increase dishonest behaviour

• 成果类型: Article
• 署名作者: Kobis, Nils; Rahwan, Zoe; Rilla, Raluca; Supriyatno, Bramantyo Ibrahim; Bersch, Clara; Ajaj, Tamer; Bonnefon, Jean-Francois; Rahwan, Iyad
• 署名单位: University of Duisburg Essen; Max Planck Society; Max Planck Society; Communaute d'universites et etablissements de Toulouse (Comue); Universite Toulouse 1 Capitole; Toulouse School of Economics
• 刊物名称: NATURE
• ISSN/ISSBN: 0028-0836; 1476-4687
• DOI: 10.1038/s41586-025-09505-x
• 发表日期: 2025-10-02
• 关键词: decisions; people

摘要

Although artificial intelligence enables productivity gains from delegating tasks to machines1, it may facilitate the delegation of unethical behaviour2. This risk is highly relevant amid the rapid rise of 'agentic' artificial intelligence systems3,4. Here we demonstrate this risk by having human principals instruct machine agents to perform tasks with incentives to cheat. Requests for cheating increased when principals could induce machine dishonesty without telling the machine precisely what to do, through supervised learning or high-level goal setting. These effects held whether delegation was voluntary or mandatory. We also examined delegation via natural language to large language models5. Although the cheating requests by principals were not always higher for machine agents than for human agents, compliance diverged sharply: machines were far more likely than human agents to carry out fully unethical instructions. This compliance could be curbed, but usually not eliminated, with the injection of prohibitive, task-specific guardrails. Our results highlight ethical risks in the context of increasingly accessible and powerful machine delegation, and suggest design and policy strategies to mitigate them.