While designing artificial intelligence (AI)-based systems, AI developers usually have to justify their design decisions and, thus, are accountable for their actions and how they design AI-based systems. Crucial facets of AI (ie, autonomy, inscrutability, and learning) notably cause potential accountability issues that AI developers must consider in their design decisions, which has received little attention in prior literature. Drawing on self-determination theory and accountability literature, we conducted a scenariobased survey (n= 132). We show that AI developers who perceive themselves as accountable tend to design AI-based systems to be less autonomous and inscrutable but more capable of learning when deployed. Our mediation analyses suggest that perceived job autonomy can partially explain these direct effects. Therefore, AI design decisions depend on individual and organizational settings and must be considered from different perspectives. Thus, we contribute to a better understanding of the effects of AI developers’ perceived accountability when designing AI-based systems.