授权
作弊
计算机科学
人工智能
背景(考古学)
激励
不诚实
顺从(心理学)
计算机安全
执行
知识管理
人类智力
业务
自动化
自然语言
语言变化
公共关系
机器学习
风险分析(工程)
作者
Nils Köbis,Zoe Rahwan,Raluca Rilla,Bramantyo Ibrahim Supriyatno,Clara Bersch,Tamer Ajaj,Jean‐François Bonnefon,Iyad Rahwan
出处
期刊:Nature
[Nature Portfolio]
日期:2025-09-17
卷期号:646 (8083): 126-134
被引量:14
标识
DOI:10.1038/s41586-025-09505-x
摘要
Abstract Although artificial intelligence enables productivity gains from delegating tasks to machines 1 , it may facilitate the delegation of unethical behaviour 2 . This risk is highly relevant amid the rapid rise of ‘agentic’ artificial intelligence systems 3,4 . 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 models 5 . 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.
科研通智能强力驱动
Strongly Powered by AbleSci AI