Delegation to artificial intelligence can increase dishonest behaviour

Although artificial intelligence enables productivity gains from delegating tasks to machines<sup>1</sup>, it may facilitate the delegation of unethical behaviour<sup>2</sup>. This risk is highly relevant amid the rapid rise of 'agentic' artificial intelligence systems<sup>3,4</sup>. 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<sup>5</sup>. 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.

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Publication Details

Journal
Nature
Published
2025-09-17
DOI
https://doi.org/10.1038/s41586-025-09505-x
Citations
8
Primary Topic
Experimental Behavioral Economics Studies
Type
article
Field-Weighted Citation Impact
18.93

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article

Delegation to artificial intelligence can increase dishonest behaviour

Iyad Rahwan, Jean‐François Bonnefon, Zoe Rahwan, Nils Köbis et al.
8 citations
Nature
Experimental Behavioral Economics Studies
18.93
article

Delegation to artificial intelligence can increase dishonest behaviour

Iyad Rahwan, Jean‐François Bonnefon, Zoe Rahwan, Nils Köbis, Tamer Ajaj, Clara Bersch, Bramantyo Ibrahim Supriyatno, Raluca Rilla
article en
8 citations

Abstract

Although artificial intelligence enables productivity gains from delegating tasks to machines<sup>1</sup>, it may facilitate the delegation of unethical behaviour<sup>2</sup>. This risk is highly relevant amid the rapid rise of 'agentic' artificial intelligence systems<sup>3,4</sup>. 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<sup>5</sup>. 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.

NatureVol. 646(8083)
Centre National de la Recherche Scientifique (FR), Toulouse School of Economics (FR), Max Planck Institute for Human Development (DE), University of Duisburg-Essen (DE)
Agence Nationale de la Recherche
Openalex Percentile: Top 1%
Experimental Behavioral Economics Studies
18.93
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