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REVIEW article

Front. Psychol.
Sec. Organizational Psychology
Volume 15 - 2024 | doi: 10.3389/fpsyg.2024.1390182

Advice From Artificial Intelligence: A Review and Practical Implications

Provisionally accepted
Julia I. Baines Julia I. Baines 1*Reeshad S. Dalal Reeshad S. Dalal 1Ho-Chun Tsai Ho-Chun Tsai 2Lida P. Ponce Lida P. Ponce 1
  • 1 George Mason University, Fairfax, United States
  • 2 Illinois Institute of Technology, Chicago, Illinois, United States

The final, formatted version of the article will be published soon.

    Despite considerable behavioral and organizational research on advice from human advisors, and despite the increasing study of artificial intelligence (AI) in organizational research, workplace-related applications, and popular discourse, an interdisciplinary review of advice from AI (vs. human) advisors has yet to be undertaken. We argue that the increasing adoption of AI to augment human decision-making would benefit from a framework that can characterize such interactions. Thus, the current research invokes judgment and decision-making research on advice from human advisors and uses a conceptual “fit”-based model to: (1) summarize how the characteristics of the AI advisor, human decision-maker, and advice environment influence advice exchanges and outcomes (including informed speculation about the durability of such findings in light of rapid advances in AI technology), (2) delineate future research directions (along with specific predictions), and (3) provide practical implications involving the use of AI advice by human decision-makers in applied settings.

    Keywords: artificial intelligence, algorithm, Chatbot, advice, ADVISOR, Robo-advisor, Virtual assistant, Anthropomorphize

    Received: 22 Feb 2024; Accepted: 29 Jul 2024.

    Copyright: © 2024 Baines, Dalal, Tsai and Ponce. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Julia I. Baines, George Mason University, Fairfax, United States

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.