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Algorithmic decision-making systems (ADMs) support an ever-growing number of decision-making processes. We conducted an online survey study in Flanders (n = 1,082) to understand how laypeople perceive and trust health ADMs. Inspired by the ability, benevolence, and integrity trustworthiness model (Mayer et al., 1995), this study investigated how trust is constructed in health ADMs. In addition, we investigated how trust construction differs between ADA Health (a self-diagnosis medical chatbot) and IBM Watson Oncology (a system that suggests treatments for cancer in hospitals). Our results show that accuracy and fairness are the biggest predictors of trust in both ADMs, whereas control plays a smaller yet significant role. Interestingly, control plays a bigger role in explaining trust in ADA Health than IBM Watson Oncology. Moreover, how appropriate people evaluate data-driven healthcare and how concerned they are with algorithmic systems prove to be good predictors for accuracy, fairness, and control in these specific health ADMs. The appropriateness of data-driven healthcare had a bigger effect with IBM Watson Oncology than with ADA Health. Overall, our results show the importance of considering the broader contextual, algorithmic, and case-specific characteristics when investigating trust construction in ADMs.

Algorithmic systems such as Learning Analytics (LA) are driving the datafication and algorithmization of education. In this research, we focus on the appropriateness of LA systems from the perspective of parents and students in secondary education. Anchored in the contextual integrity framework (Nissenbaum, "Washington Law Review, 79," 41, 2004), we conducted two survey studies (N[subscript students]=277, N[subscript parents]=1013) in Flanders to investigate how they evaluate the appropriateness of the data flows in LA systems, and how both populations differ in their evaluations. The results show that the most-used student-centered LA are perceived less appropriate than the less-used teacher-centered LA by both students and parents. The usage of personal characteristics in LA is perceived as least appropriate, in contrast to coarser class characteristics. Sharing insights of LA with institutions that are part of the traditional educational context, such as the school, is seen as the most appropriate, and more appropriate than sharing it with learning platforms or third parties (e.g., Big Tech). Overall, we found that parents evaluated the different elements of the dataflows embedded in LA as less appropriate than students. In the discussion, we argue that educational institutions should include the evaluation of both parents and students to further manage expectations and construct shared norms and practices when implementing LA in education.informa