Building Trust with a Teachable Artificial Intelligence: The Case of Repeated Trust Games

This study explores the teaching of Artificial Intelligence (AI) systems in a repeated trust game. We evaluate whether participants trust the AI and teach it to adopt the most beneficial strategies among a set of four options with different levels of benefit. Results indicate that participants are initially cautious with the AI but increase their trust throughout the experiment, especially those with initially low trust. Participants imperfectly teach the AI, initially adopting beneficial strategies, then progressively learning the most advantageous ones while discarding those that offer minimal or no benefit. We also observe that participants inefficiently choose to avoid positive learning when it involves a risk of large losses. In additional tasks, we observe that participants naturally seek ways to exploit the AI, although a significant portion maintains human fairness in the interaction. Moreover, they effectively transfer prior knowledge to similar tasks. We conclude that participants trust an AI they can teach to generate significant benefits, although the risk of loss limits this trust and the efficiency of teaching.