"Why Do I Care What's Similar?" Probing Challenges in AI-Assisted Child Welfare Decision-Making through Worker-AI Interface Design Concepts

Abstract

Data-driven AI systems are increasingly used to augment human decision-making in complex, social contexts, such as social work or legal practice. Yet, most existing design knowledge regarding how to best support AI-augmented decision-making comes from studies in comparatively well-defined settings. In this paper, we present findings from design interviews with 12 social workers who use an algorithmic decision support tool (ADS) to assist their day-to-day child maltreatment screening decisions. We generated a range of design concepts, each envisioning different ways of redesigning or augmenting the ADS interface. Overall, workers desired ways to understand the risk score and incorporate contextual knowledge, which move beyond existing notions of AI interpretability. Conversations around our design concepts also surfaced more fundamental concerns around the assumptions underlying statistical prediction, such as inference based on similar historical cases and statistical notions of uncertainty. Based on our findings, we discuss how ADS may be better designed to support the roles of human decision-makers in social decision-making contexts.