A Frictional Design Approach: Towards Judicial AI and its Possible Applications

Decision support systems (DSS) are increasingly being integrated into high-stakes domains like healthcare, law, and finance, where critical decisions have significant consequences. Traditional DSS often provide a single, clear-cut recommendation, which can lead to automation bias and diminish the user’s sense of agency. However, there is a growing concern about the over-reliance on these systems and the potential for deskilling among users. The knowledge gap we aim to address is the development of decision support systems that effectively encourage critical reflection and maintain user engagement and responsibility in decision-making processes. In this workshop contribution, we report on the development of Judicial AI, a novel approach inspired by Frictional AI. Judicial AI diverges from traditional DSS by offering multiple, contrasting explanations to support different potential outcomes. This design encourages users to engage in deeper cognitive processing, thereby promoting critical reflection, reducing automation bias, and preserving the user’s sense of agency. This ongoing study employs a two-arm experiment to investigate the effects of this approach in the context of content classification tasks, comparing it with the traditional protocol. The expected outcomes of this ongoing study suggest that the Judicial protocol could not only mitigate automation bias but also safeguard users’ sense of agency and promote long-term skill retention.