Jurgis Karpus

People are keen to exploit cooperative artificial agents for selfish gain. While this phenomenon has been observed in numerous Western societies, we show here that it is absent in Japan. We examined people’s willingness to cooperate with artificial agents and humans in two classic economic games requiring a choice between self interest and mutual benefit. Our participants in the United States cooperated with artificial agents significantly less than they did with humans, whereas participants in Japan exhibited equivalent levels of cooperation with both types of co-player. We found a notable difference in how people felt about exploiting their cooperative partner: people in Japan emotionally treated artificial agents and humans alike, whereas people in the United States felt bad about exploiting humans, but not machines. Our findings underscore the necessity for nuanced cultural considerations in the design and implementation of such technology across diverse societies.

The game theoretic notion of best-response reasoning is sometimes criticized when its application produces multiple solutions of games, some of which seem less compelling than others. The recent development of the theory of team reasoning addresses this by suggesting that interacting players in games may sometimes reason as members of a team – a group of individuals who act together in the attainment of some common goal. A number of properties have been suggested for team-reasoning decision-makers’ goals to satisfy, but a few formal representations have been discussed. In this paper we suggest a possible representation of these goals based on the notion of mutual advantage. We propose a method for measuring extents of individual and mutual advantage to the interacting decision-makers, and define team interests as the attainment of outcomes associated with maximum mutual advantage in the games they play.

Cooperation between humans and machines is increasingly vital as artificial intelligence (AI) becomes integrated into daily life. Research shows that people are often less willing to cooperate with AI agents than with humans and are more likely to exploit AI for personal gain. While prior studies indicate that human-like features in AI influence cooperation, the impact of AI’s assigned gender remains underexplored. This study investigates how cooperation varies with the gender labels assigned to AI partners. In a Prisoner’s Dilemma game, 402 participants interacted with partners labeled as AI or human, and as male, female, non-binary, or gender-neutral. Participants exploited female-labeled and distrusted male-labeled AI agents more than human counterparts with the same gender labels, reflecting gender biases similar to those in human-human interactions. These findings underscore the importance of accounting for gender bias in AI design, policy, and regulation.

Creating a legacy is often seen as a way to circumvent mortality. At the very least, it is a way to ensure that someone’s ideas live on and their influence on others extends beyond their own lifetime. Common examples of such legacies are cultural products, such as books, music, or art, that one creates and leaves behind. In light of rapid advances in artificial intelligence research, it is conceivable that it will soon become possible – and, to some extent, it already is – to create a new type of legacy in the form of a virtual entity, such as a chatbot or an avatar, that acts as a digital replica of someone. In this paper, we investigate in what sense, if any, one could say that the original person continues to exist in their digital replica. Taking Derek Parfit’s work on the nature of personal identity as our inspiration and starting point, we suggest under what circumstances one could (and should) perceive such replicas as genuine extensions of one's personhood.

The game theoretic notion of best-response reasoning is sometimes criticized when its application produces multiple solutions of games, some of which seem less compelling than others. The recent development of the theory of team reasoning addresses this by suggesting that interacting players in games may sometimes reason as members of a team – a group of individuals who act together in the attainment of some common goal. A number of properties have been suggested for team-reasoning decision-makers’ goals to satisfy, but a few formal representations have been discussed. In this paper we suggest a possible representation of these goals based on the notion of mutual advantage. We propose a method for measuring extents of individual and mutual advantage to the interacting decision-makers, and define team interests as the attainment of outcomes associated with maximum mutual advantage in the games they play.

The game theoretic notion of best-response reasoning is sometimes criticized when its application produces multiple solutions of games, some of which seem less compelling than others. The recent development of the theory of team reasoning addresses this by suggesting that interacting players in games may sometimes reason as members of a team – a group of individuals who act together in the attainment of some common goal. A number of properties have been suggested for team-reasoning decision-makers’ goals to satisfy, but a few formal representations have been discussed. In this paper we suggest a possible representation of these goals based on the notion of mutual advantage. We propose a method for measuring extents of individual and mutual advantage to the interacting decision-makers, and define team interests as the attainment of outcomes associated with maximum mutual advantage in the games they play.

Nudge is a popular public policy tool that harnesses well-known biases in human judgement to subtly guide people’s decisions, often to improve their choices or to achieve some socially desirable outcome. Thanks to recent developments in artificial intelligence (AI) methods new possibilities emerge of how and when our decisions can be nudged. On the one hand, algorithmically personalized nudges have the potential to vastly improve human daily lives. On the other hand, blindly outsourcing the development and implementation of nudges to “black box” AI systems means that the ultimate reasons for why such nudges work, that is, the underlying human cognitive processes that they harness, will often be unknown. In this paper, we unpack this concern by considering a series of examples and case studies that demonstrate how AI systems can learn to harness biases in human judgment to reach a specified goal. Drawing on an analogy in a philosophical debate concerning the methodology of economics, we call for the need of an interdisciplinary oversight of AI systems that are tasked and deployed to nudge human behaviours.

We cooperate with other people despite the risk of being exploited or hurt. If future artificial intelligence (AI) systems are benevolent and cooperative toward us, what will we do in return? Here we show that our cooperative dispositions are weaker when we interact with AI. In nine experiments, humans interacted with either another human or an AI agent in four classic social dilemma economic games and a newly designed game of Reciprocity that we introduce here. Contrary to the hypothesis that people mistrust algorithms, participants trusted their AI partners to be as cooperative as humans. However, they did not return AI's benevolence as much and exploited the AI more than humans. These findings warn that future self-driving cars or co-working robots, whose success depends on humans' returning their cooperativeness, run the risk of being exploited. This vulnerability calls not just for smarter machines but also better human-centered policies.

An effective method to increase the number of potential cadaveric organ donors is to make people donors by default with the option to opt out. This non-coercive public policy tool to influence people’s choices is often justified on the basis of the as-judged-by-themselves principle: people are nudged into choosing what they themselves truly want. We review three often hypothesized reasons for why defaults work and argue that the as-judged-by-themselves principle may hold only in two of these cases. We specify further conditions for when the principle can hold in these cases and show that whether those conditions are met is often unclear. We recommend ways to expand nationwide surveys to identify the actual reasons for why defaults work and discuss mandated choice policy as a viable solution to many arising conundrums.