Managing AI Advice in Crowd Decision-Making

Dominik Walzner, Andreas Fügener, Alok Gupta

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

In this paper, we examine how advice from an AI algorithm should be provided to decision-makers that work in a crowd setting. With a theoretical model and numerical experiments we show that the harmful effect of incorrect advice relative to the beneficial effect of correct advice increases with increasing crowd size. Thus, for larger crowds, more advice should be withheld so that it does not negatively affect the crowd accuracy. We propose a mechanism for AI advice personalization that takes the crowd size into account. In an experimental study where subjects classified images, we demonstrate that the crowd size-dependent advice personalization reduces the detrimental effects of incorrect advice and leads to an increase in crowd accuracy.

Original languageEnglish (US)
Title of host publicationInternational Conference on Information Systems, ICIS 2022
Subtitle of host publication"Digitization for the Next Generation"
PublisherAssociation for Information Systems
ISBN (Electronic)9781713893615
StatePublished - 2022
Event43rd International Conference on Information Systems: Digitization for the Next Generation, ICIS 2022 - Copenhagen, Denmark
Duration: Dec 9 2022Dec 14 2022

Publication series

NameInternational Conference on Information Systems, ICIS 2022: "Digitization for the Next Generation"

Conference

Conference43rd International Conference on Information Systems: Digitization for the Next Generation, ICIS 2022
Country/TerritoryDenmark
CityCopenhagen
Period12/9/2212/14/22

Bibliographical note

Publisher Copyright:
© 2022 International Conference on Information Systems, ICIS 2022: "Digitization for the Next Generation". All Rights Reserved.

Keywords

  • advice taking
  • Human-AI collaboration
  • wisdom-of-crowds

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