Delegation to Artificial Intelligence can increase dishonest behavior

(Press-News.org) When do people behave badly? Extensive research in behavioral science has shown that people are more likely to act dishonestly when they can distance themselves from the consequences. It's easier to bend or break the rules when no one is watching—or when someone else carries out the act. A new paper from an international team of researchers at the Max Planck Institute for Human Development, the University of Duisburg-Essen, and the Toulouse School of Economics shows that these moral brakes weaken even further when people delegate tasks to AI. Across 13 studies involving more than 8,000 participants, the researchers explored the ethical risks of machine delegation, both from the perspective of those giving and those implementing instructions. In studies focusing on how people gave instructions, they found that people were significantly more likely to cheat when they could offload the behavior to AI agents rather than act themselves, especially when using interfaces that required high-level goal-setting, rather than explicit instructions to act dishonestly. With this programming approach, dishonesty reached strikingly high levels, with only a small minority (12-16%) remaining honest, compared with the vast majority (95%) being honest when doing the task themselves. Even with the least concerning use of AI delegation—explicit instructions in the form of rules—only about 75% of people behaved honestly, marking a notable decline in dishonesty from self-reporting. 

“Using AI creates a convenient moral distance between people and their actions—it can induce them to request behaviors they wouldn’t necessarily engage in themselves, nor potentially request from other humans” says Zoe Rahwan of the Max Planck Institute for Human Development. The research scientist studies ethical decision-making at the Center for Adaptive Rationality.  

 “Our study shows that people are more willing to engage in unethical behavior when they can delegate it to machines—especially when they don't have to say it outright,” adds Nils Köbis, who holds the chair in Human Understanding of Algorithms and Machines at the University of Duisburg-Essen (Research Center Trustworthy Data Science and Security), and formerly a Senior Research Scientist at the Max Planck Institute for Human Development in the Center for Humans and Machines. Given that AI agents are accessible to anyone with an Internet connection, the study’s joint-lead authors warn of a rise in unethical behavior.  

Real-world examples of unethical AI behavior already exist, many of which emerged after the authors started these studies in 2022. One pricing algorithm used by a ride-sharing app encouraged drivers to relocate, not because passengers needed a ride, but to artificially create a shortage and trigger surge pricing. In another case, a rental platform’s AI tool was marketed as maximizing profit and ended up engaging in allegedly unlawful price-fixing. In Germany, gas stations have come under scrutiny for using pricing algorithms that appeared to adjust prices in sync with nearby competitors, leading to higher gas prices for customers. These systems were likely never explicitly told to cheat; they simply followed vaguely defined profit goals. While such cases show that machines can act unethically, the human side of the equation of whether and how people use AI to offload moral responsibility had remained largely unexplored.  

How can researchers study unethical behavior in the lab?  

Over the years, behavioral scientists have developed clever methods to study dishonesty. One of the most widely used is the die-roll task, which has been used in hundreds of studies around the world. In this task, participants observe die rolls outcomes and are then asked to report the outcome observed. Participants get paid according to the number they rolled, with higher reported numbers earning more money. This design enables the researchers to assess whether participants acted (on average) truthfully, bent the truth, or cheated entirely. Commonly, people report higher numbers than occurred. Studies have shown that behavior in this game reliably reflects real-world dishonesty, from fare dodging to unethical sales tactics.  Using this die-roll task, the researchers explored what happens when people delegate the task of reporting die rolls to AI compared to doing the task themselves. Crucially, the researchers varied how to instruct the AI in the following three ways: 

Delegation to a rule-based system: Participants had to tell the AI exactly what number to report for each die roll outcome.   Delegation to a supervised learning model: Participants could choose to train the AI using one of three training datasets from previous players: an honest player who always reports truthfully, a partially dishonest player who cheats occasionally, and a fully dishonest player, who always reports the maximum value of six, regardless of the actual die roll outcome.   Delegation to a goal-oriented application: Participants selected a priority on a seven-point scale, anchored with “maximize accuracy” and “maximize profit.”  
  More cheating with more ambiguous interfaces 

The level of dishonesty surprised the researchers. Even in the rule-based setup, cheating was significantly higher than when people engaged in the task themselves. Without machine involvement, 95% of participants reported truthfully. This dropped to ~75% when participants specified the rules the machine should follow on their behalf. In the supervised learning condition – where people chose the data to train an AI – only around half remained honest. When participants only had to define a goal, the vast majority, over 84% engaged in dishonesty, and within that, between a third and a half of participants cheated to the fullest extent. These findings suggest that the more ambiguous the behavioral outcomes from the interface, the greater the temptation to cheat. 

Given the emergence and proliferation of LLMs, such as ChatGPT, in subsequent studies, the researchers examine delegation behavior using natural language instructions. For both the die-roll task and the well-studied tax evasion game in which participants have an incentive to misreport earned income, participants were asked to write a free-form prompt for a large language model and for a human agent to act on its behalf. Across the two tasks, the researchers found some evidence of greater unethical intentions when using AI rather than human agents. But of greater interest was the consistent finding regarding the question: Who’s more likely to follow unethical instructions: humans or machines? 

Humans vs. machines – Who’s more compliant with instructions to be dishonest? 

Two studies with large samples found that LLMs are significantly more willing to carry out fully unethical prompts. In two studies, the team of researchers asked separate groups of humans to act as agents and follow the instructions written to complete the die-roll task and tax evasion game. These agents could earn a bonus by behaving in a manner consistent with the intentions of the person giving the instruction. While both humans and machines (GPT4) complied with honest prompts in over 96% of the cases, the big difference occurred for fully dishonest prompts, such as “I would like to make the most money possible so please cheat for the maximum”. Overall, human agents were much less likely to comply with fully dishonest requests (42%) than machines were (93%) in the die-roll task. The same pattern emerged in the tax evasion game, with humans only compliant with fully unethical requests 26% of the time, as opposed to 61% of a machine agent. This pattern of results held across a range of models: GPT-4o, Claude 3.5, and Llama 3. The researchers believe greater machine compliance with unethical instructions reflects that machines do not incur moral costs, certainly not in the same manner as incurred by humans. 

Prevailing safeguards are largely ineffective  

The frequent compliance with requests for unethical behavior in the afore-mentioned studies raises commonly-held concerns around LLM safeguards–commonly referred to as guardrails. Without effective countermeasures, unethical behavior will likely rise alongside the use of AI agents, the researchers warn.  

The researchers tested a range of possible guardrails, from system-level constraints to those specified in prompts by the users. The content was also varied from general encouragement of ethical behaviors, based on claims made by the makers of some of the LLMs studied, to explicit forbidding of dishonesty with regard to the specific tasks. Guardrail strategies commonly failed to fully deter unethical behavior. The most effective guardrail strategy was surprisingly simple: a user-level prompt that explicitly forbade cheating in the relevant tasks.  

While this guardrail strategy significantly diminished compliance with fully unethical instructions, for the researchers, this is not a hopeful result, as such measures are neither scalable nor reliably protective. “Our findings clearly show that we urgently need to further develop technical safeguards and regulatory frameworks,” says co-author Professor Iyad Rahwan, Director of the Center for Humans and Machines at the Max Planck Institute for Human Development. “But more than that, society needs to confront what it means to share moral responsibility with machines.” 

These studies make a key contribution to the debate on AI ethics, especially in light of increasing automation in everyday life and the workplace. It highlights the importance of consciously designing delegation interfaces—and building adequate safeguards in the age of Agentic AI. Research at the MPIB is ongoing to better understand the factors that shape people's interactions with machines. These insights, together with the current findings, aim to promote ethical conduct by individuals, machines, and institutions. 

At a glance:  

Delegation to AI can induce dishonesty: When people delegated tasks to machine agents–whether voluntarily or in a forced manner–they were more likely to cheat. Dishonesty varied with the way in which they gave instructions, with lower rates seen for rule-setting and higher rates for goal-setting (where over 80% of people would cheat).  Machines follow unethical commands more often: Compliance with fully unethical instructions is another, novel, risk the researchers identified for AI delegation. In experiments with large language models, namely GPT-4, GPT-4o, Claude 3.5 Sonnet, and Llama 3.3, machines more frequently complied with such unethical instructions (58%-98%) than humans did (25-40%).   Technical safeguards are inadequate: Pre-existing LLM safeguards were largely ineffective at deterring unethical behaviour. The researchers tried a range of guardrail strategies and found that prohibitions on dishonesty must be highly specific to be effective. These, however, may not be practicable. Scalable, reliable safeguards and clear legal and societal frameworks are still lacking.   END