Explainable AI and Adoption of Financial Algorithmic Advisors: An Experimental Study

Daniel Ben David, Yehezkel S. Resheff, Talia Tron

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

10 Scopus citations

Abstract

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 "No-explanation"alternative. These results add to the literature on the importance of XAI for algorithmic adoption and trust.

Original languageAmerican English
Title of host publicationAIES 2021 - Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society
PublisherAssociation for Computing Machinery, Inc
Pages390-400
Number of pages11
ISBN (Electronic)9781450384735
DOIs
StatePublished - 21 Jul 2021
Externally publishedYes
Event4th AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society, AIES 2021 - Virtual, Online, United States
Duration: 19 May 202121 May 2021

Publication series

NameAIES 2021 - Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society

Conference

Conference4th AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society, AIES 2021
Country/TerritoryUnited States
CityVirtual, Online
Period19/05/2121/05/21

Bibliographical note

Publisher Copyright:
© 2021 ACM.

Keywords

  • algorithm adoption
  • experiment
  • explainable ai
  • financial advice
  • hci
  • trust

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