  We study whether receiving advice from either a human or algorithmic advisor,
accompanied by five types of Local and Global explanation labelings, has an
effect on the readiness to adopt, willingness to pay, and trust in a financial
AI consultant. We compare the differences over time and in various key
situations using a unique experimental framework where participants play a
web-based game with real monetary consequences. We observed that accuracy-based
explanations of the model in initial phases leads to higher adoption rates.
When the performance of the model is immaculate, there is less importance
associated with the kind of explanation for adoption. Using more elaborate
feature-based or accuracy-based explanations helps substantially in reducing
the adoption drop upon model failure. Furthermore, using an autopilot increases
adoption significantly. Participants assigned to the AI-labeled advice with
explanations were willing to pay more for the advice than the AI-labeled advice
with a No-explanation alternative. These results add to the literature on the
importance of XAI for algorithmic adoption and trust.
