Explainable AI and Adoption of Financial Algorithmic Advisors: an Experimental Study
This research addresses the challenge of improving user adoption and trust in financial algorithmic advisors for individuals, particularly concerning the role of explainable AI.
This study investigates how different types of explanations (Local and Global) for financial AI advisors affect user adoption, willingness to pay, and trust. It found that initial accuracy-based explanations lead to higher adoption rates, and more elaborate feature-based or accuracy-based explanations significantly reduce adoption drops after model failure. Additionally, an autopilot feature increased adoption, and users were willing to pay more for AI advice with explanations.
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.