Explainable AI and Adoption of Algorithmic Advisors: an Experimental Study

Machine learning is becoming a commonplace part of our technological experience. The notion of explainable AI (XAI) is attractive when regulatory or usability considerations necessitate the ability to back decisions with a coherent explanation. A large body of research has addressed algorithmic methods of XAI, but it is still unclear how to determine what is best suited to create human cooperation and adoption of automatic systems. Here we develop an experimental methodology where participants play a web-based game, during which they receive advice from either a human or algorithmic advisor, accompanied with explanations that vary in nature between experimental conditions. We use a reference-dependent decision-making framework, evaluate the game results over time, and in various key situations, to determine whether the different types of explanations affect the readiness to adopt, willingness to pay and trust a financial AI consultant. We find that the types of explanations that promotes adoption during first encounter differ from those that are most successful following failure or when cost is involved. Furthermore, participants are willing to pay more for AI-advice that includes explanations. These results add to the literature on the importance of XAI for algorithmic adoption and trust.