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 failureor 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.
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.
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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
We study whether receiving
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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
by five types of Local and Global explanation labelings, has an effect on the
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and trust
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find that the types of explanations that promotes adoption during first encounter differ from those that are most successful following failureor when cost is involved
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
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participants are
using an autopilot increases adoption significantly. Participants assigned to the AI-labeled advice with explanations were
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AI-advice that includes explanations
the advice than the AI-labeled advice with a No-explanation alternative