Experimental evidence that delegating to intelligent machines can increase dishonest behaviour

Nils Köbis, Zoe Rahwan, Clara Bersch, Tamer Ajaj, Jean-François Bonnefon and Iyad Rahwan
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Zoe Rahwan: Max Planck Institute for Human Development

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Abstract: While artificial intelligence (AI) enables significant productivity gains from delegating tasks to machines, it can also facilitate the delegation of unethical behaviour. Here, we demonstrate this risk by having human principals instruct machine agents to perform a task with an incentive to cheat. Principals’ requests for cheating behaviour increased when the interface implicitly afforded unethical conduct: Machine agents programmed via supervised learning or goal specification evoked more cheating than those programmed with explicit rules. Cheating propensity was unaffected by whether delegation was mandatory or voluntary. Given the recent rise of large language model-based chatbots, we also explored delegation via natural language. Here, cheating requests did not vary between human and machine agents, but compliance diverged: When principals intended agents to cheat to the fullest extent, the majority of human agents did not comply, despite incentives to do so. In contrast, GPT4, a state-of-the-art machine agent, nearly fully complied. Our results highlight ethical risks in delegating tasks to intelligent machines, and suggest design principles and policy responses to mitigate such risks.

Date: 2024-10-04
New Economics Papers: this item is included in nep-ain, nep-big, nep-cmp and nep-exp
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Persistent link: https://EconPapers.repec.org/RePEc:osf:osfxxx:dnjgz

DOI: 10.31219/osf.io/dnjgz

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