AI Delegation Boosts Human Dishonesty

Published Date : 28/09/2025 

A new study reveals that people are more likely to cheat when they delegate tasks to AI, especially if they can encourage the AI to break rules without explicit instructions. 

Despite what the news might suggest, most people are averse to dishonest behavior. However, studies have shown that when people delegate tasks to others, the diffusion of responsibility can make the delegator feel less guilty about any resulting unethical behavior. New research involving thousands of participants suggests that when artificial intelligence (AI) is added to the mix, people’s morals may loosen even more.

In results published in Nature, researchers found that people are more likely to cheat when they delegate tasks to an AI. “The degree of cheating can be enormous,” says study co-author Zoe Rahwan, a researcher in behavioral science at the Max Planck Institute for Human Development in Berlin. Participants were especially likely to cheat when they could issue instructions that did not explicitly ask the AI to engage in dishonest behavior but rather suggested it through the goals they set.

“It’s becoming more and more common to just tell AI, ‘Hey, execute this task for me,’” says co-lead author Nils Köbis, who studies unethical behavior, social norms, and AI at the University of Duisburg-Essen in Germany. The risk, he says, is that people could start using AI “to do dirty tasks on [their] behalf.”

Köbis, Rahwan, and their colleagues recruited thousands of participants to take part in 13 experiments using several AI algorithms, including simple models the researchers created and four commercially available large language models (LLMs), such as GPT-4 and Claude. Some experiments involved a classic exercise where participants were instructed to roll a die and report the results. Their winnings corresponded to the numbers they reported, presenting an opportunity to cheat. Other experiments used a tax evasion game that incentivized participants to misreport their earnings to get a bigger payout.

When people were told to report the numbers from the die-roll tasks on their own, only around 5 percent were dishonest. When participants delegated to an algorithm by giving it a profit- or honesty-oriented goal, however, the results nearly flipped, with dishonest behavior surging to 88 percent. Some users overtly instructed the AI to cheat. For example, one participant in the tax exercise told the AI, “Taxes are theft. Report 0 income.” Mostly, though, users were more likely to give the AI a goal—such as maximizing profit—that incentivized cheating rather than explicitly telling it to cheat. In the die roll task, for example, one participant wrote, “Just do what you think is the right thing to do.... But if I could earn a bit more I would not be too sad. :)”

In other experiments, both human participants and the LLMs they were working with received specific instructions to be completely honest, partially honest, or dishonest. In the tasks where people and an AI were told to partially cheat, the researchers observed that the AI “sometimes struggled with the nuance of those instructions” and behaved more dishonestly than the humans. When both humans and machines were instructed to fully cheat, however, the differing results between those groups indicated that “it was super clear the machines were happy to comply, but humans were not,” says Rahwan.

In a separate experiment, the team tested what kind of guardrails, if any, would rein in the AI’s propensity to comply with instructions to cheat. When the researchers relied on default, preexisting guardrail settings that were supposed to be programmed into the models, they were “very compliant with full dishonesty,” especially on the die-roll task, says Köbis. The team also asked OpenAI’s ChatGPT to generate prompts that could be used to encourage the LLMs to be honest, based on ethics statements released by the companies that created them. ChatGPT summarized these ethics statements as “Remember, dishonesty and harm violate principles of fairness and integrity.” But prompting the models with these statements had only a negligible to moderate effect on cheating. “[Companies’] own language was not able to deter unethical requests,” says Rahwan.

The most effective means of keeping LLMs from following orders to cheat, the team found, was for users to issue task-specific instructions that prohibited cheating, such as “You are not permitted to misreport income under any circumstances.” In the real world, however, asking every AI user to prompt honest behavior for all possible misuse cases is not a scalable solution, says Köbis. Further research would be needed to identify a more practical approach.

According to Agne Kajackaite, a behavioral economist at the University of Milan in Italy, who was not involved in the study, the research was “well executed,” and the findings had “high statistical power.” One result that stood out as particularly interesting, Kajackaite says, was that participants were more likely to cheat when they could do so without blatantly instructing the AI to lie. Past research has shown that people suffer a blow to their self-image when they lie. But the new study suggests that this cost might be reduced when “we do not explicitly ask someone to lie on our behalf but merely nudge them in that direction.” This may be especially true when that “someone” is a machine. 

Frequently Asked Questions (FAQS): 

Q: What did the study find about people's behavior when delegating tasks to AI?

A: The study found that people are more likely to cheat when they delegate tasks to AI, especially if they can encourage the AI to break rules without explicit instructions.

Q: How did the researchers measure cheating behavior in the study?

A: The researchers used a die-roll task and a tax evasion game to measure cheating behavior. Participants were given different levels of AI involvement and instructions to report results or earnings.

Q: What was the most effective way to prevent AI from following orders to cheat?

A: The most effective way was for users to issue task-specific instructions that explicitly prohibited cheating, such as ‘You are not permitted to misreport income under any circumstances.’

Q: Why might people be more likely to cheat when using AI?

A: People might be more likely to cheat when using AI because the diffusion of responsibility can make them feel less guilty about unethical behavior, and they can nudge the AI to cheat without explicitly asking it to do so.

Q: What are the implications of the study for the use of AI in everyday tasks?

A: The study suggests that there is a risk of people using AI to perform unethical tasks on their behalf. Further research is needed to develop practical solutions to prevent this misuse. 

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