  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.
