Trust in algorithmic decision-making systems in health: A comparison between ADA health and IBM Watson.

Abstract

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.