Will People Try to Shift Moral Responsibility For Their Unfair Decisions Onto AI Agents? An Experiment Seeks to Find Out
Authored by Stephan Tontrup and Christopher Jon Sprigman
People hire agents for many reasons—from selling houses to negotiating contracts—not only to obtain expertise, but also to reduce the moral responsibility for the outcomes associated with hard bargaining. By delegating decisions to an agent, principals may profit while bearing fewer moral costs, as responsibility for unfair outcomes can be partially shifted to the intermediary.
This dynamic is well documented in laboratory experiments. In the standard dictator game, one player (the Allocator) receives an endowment (e.g., $10) and unilaterally decides how much, if any, to give to a passive Recipient. Although self-interest predicts zero transfers, Allocators typically give around 30 percent, reflecting a strong fairness norm that windfalls should be shared. However, when an agent is introduced to implement the transfer, Allocators give less. Prior work shows that agents are used precisely to diffuse responsibility for unfair outcomes, thereby lowering the moral costs of norm violation.
Delegation is usually costly, which limits the extent to which individuals can offload responsibility. Advances in artificial intelligence, however, dramatically reduce these costs. Widely available artificial intelligence (AI) systems—particularly so-called agentive AI—may function as inexpensive intermediaries, raising a novel question: will people treat AI agents as morally responsible actors and use them to offload responsibility in the same way they do with human agents?
To address this question, we build on a research agenda on Behavioral Self-Management (BSM), which studies how individuals strategically redesign their decision environment to more effectively pursue their self-interest, including, as in this case, by muting the self-image and social-image costs of self-interested behavior. We investigate whether AI agents can facilitate BSM by absorbing moral responsibility for norm violations.
We test this idea experimentally in a laboratory study conducted at the NYU Stern Behavioral Lab. Participants played two dictator games with a $10 endowment: a Self-Image Game, in which decisions were private, and a Social-Image Game, in which an Observer could reward fair behavior. Participants were randomly assigned either to an AI Treatment, in which they could delegate part of the transfer process to ChatGPT, or to a Control, in which the same task was implemented using deterministic (i.e., conventional, non-AI) software (in this case, the browser-based LimeSurvey statistical survey application).
In the AI Treatment, Allocators could either execute the transfer themselves or delegate the final step to ChatGPT. To ensure that delegation reflected responsibility offloading rather than advice-seeking, ChatGPT followed a fully scripted protocol. The AI sequentially presented each possible transfer amount ($0–$10) and asked Allocators to confirm their preferred amount before executing the transfer. The protocol was explicitly explained to participants to clarify that the AI provided no evaluative or normative guidance. The Control condition used an identical protocol implemented by LimeSurvey, and participants were informed that the software was non-agentive.
The design placed AI in a role analogous to a human intermediary who merely carries out a principal’s instructions. Importantly, responsibility attribution tends to concentrate on the final actor in a causal chain. In our experiment, only the entity executing the final step—the release of a code enabling the Recipient to collect earnings—could complete the transfer. When Allocators delegated to ChatGPT, the AI revealed the code to the Recipient. When they did not, Allocators had to do so themselves. In the Control, LimeSurvey always executed the final step.
Four main results emerge. First, delegation to AI was common. In the AI Treatment, 38 percent of Allocators delegated in the Self-Image Game and 32 percent in the Social-Image Game. Delegation was substantially less frequent in the Control, indicating that participants preferred delegating to an AI agent over delegating to non-agentive software.
Second, transfers were lower in the AI Treatment than in the Control. Within the AI Treatment, Allocators who delegated gave significantly less than those who did not. This pattern is consistent with the use of AI as a responsibility buffer that facilitates norm violations.
Third, participants attributed significantly more responsibility to AI than to the deterministic software, and correspondingly less responsibility to themselves. Allocators in the AI Treatment delegated at similar rates and transferred similar amounts in both the Self-Image and Social-Image Games, suggesting that they believed AI delegation would reduce not only their own sense of responsibility, but also the responsibility attributed to them by others. This belief was objectively correct: Observers assigned responsibility to AI at levels similar to Allocators’ own attributions.
Fourth, delegation behavior followed the predictions of BSM theory. Allocators who attributed greater responsibility to AI were more likely to delegate, and higher responsibility attribution predicted lower transfers. Prosocial Allocators—who face higher moral costs when violating fairness norms—were especially likely to delegate and reduced their transfers more sharply than proself types. Participants who exhibited no other-regarding concerns did not delegate, consistent with the idea that delegation is motivated by moral, not material, considerations.
These findings have important implications for law and policy. They suggest that AI delegation can enable individuals—including otherwise prosocial ones—to engage in unethical or irresponsible behavior under the socially accepted cover of AI mediation. Organizations may strategically design or market AI systems to encourage such delegation, thereby facilitating risk-taking, norm violations, or even legal noncompliance.
More broadly, the results highlight a form of AI-induced behavioral risk that is largely absent from current regulatory frameworks. Existing regulations, such as the EU AI Act, focus on risks inherent to AI systems themselves—bias, accuracy, safety—but pay little attention to how AI reshapes human responsibility attribution and moral decision-making. Closing formal accountability gaps between developers, deployers, and users may not suffice to address these social accountability gaps. Constraining the strategic use of AI to offload responsibility may therefore require new forms of legal and institutional intervention.
Stephan Tontrup is the Lawrence Jacobson Fellow of Law and Business, New York University School of Law. Christopher Jon Sprigman is the Murray and Kathleen Bring Professor of Law, and Co-Director of the Engelberg Center on Innovation Law and Policy at New York University School of Law. This post is based on their forthcoming paper, Strategic Delegation of Moral Decisions to AI.