Navigating Cooperation in Multiuser AI Systems: Procedural Justice, Explainable AI, and Social Dynamics

As Multiuser Artificial Intelligence Systems (MAIS) play an increasing role in the public sphere, user cooperation is crucial for their goal. Since the public sphere is diverse in users, interests, and features, a sociotechnical perspective is needed to examine the cooperation with the MAIS. Therefore, building on the Procedural Justice Theory, we propose two pathways for the user to perceive procedural justice. First, explainable AI (XAI) can support the perception of the procedural justice's pillars. Second is social identity, where users with strong community ties perceive fairness regardless of the explanation. Last, we hypothesize that XAI and the user’s social identity interact to affect cooperation. Through a scenario survey, we aim to explore how different types of XAI and user identifications affect users’ intention to cooperate with MAISs. Our findings can contribute to the XAI and justice literature and highlight how policymakers can design effective MAIS.