How do Humans Interact with Algorithms? Experimental Evidence from Health Insurance

Abstract

Algorithms are increasingly available to help consumers make purchasing decisions. How does algorithmic advice affect human decisions and what types of consumers are likely to use such advice? We use data from a randomized controlled trial of algorithmic advice in the context of prescription drug insurance to examine these questions. We propose that algorithmic recommendations can affect decision-making by influencing consumer beliefs about either product features (learning) or how to value those features (interpretation). We use data from the trial to estimate the importance of each mechanism. We find evidence that algorithms influence choices through both channels. Further, we document substantial selection into the use of algorithmic expert advice. Consumers who we predict would have responded more to algorithmic advice were less likely to demand it. Our results raise concerns regarding the ability of algorithmic advice to alter consumer preferences as well as the distributional implications of greater access to algorithmic advice.