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Appraisals of harms and injustice trigger an eerie feeling that decreases trust in artificial intelligence systems

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  • Yulia Sullivan

    (Baylor University)

  • Marc Bourmont

    (NEOMA Business School)

  • Mary Dunaway

    (Morgan State University)

Abstract

As artificial intelligence (AI) becomes more pervasive, the concern over how users can trust artificial agents is more important than ever before. In this research, we seek to understand the trust formation between humans and artificial agents from the morality and uncanny theory perspective. We conducted three studies to carefully examine the effect of two moral foundations: perceptions of harm and perceptions of injustice, as well as reported wrongdoing on uncanniness and examine the effect of uncanniness on trust in artificial agents. In Study 1, we found perceived injustice was the primary determinant of uncanniness and uncanniness had a negative effect on trust. Studies 2 and 3 extended these findings using two different scenarios of wrongful acts involving an artificial agent. In addition to explaining the contribution of moral appraisals to the feeling of uncanny, the latter studies also uncover substantial contributions of both perceived harm and perceived injustice. The results provide a foundation for establishing trust in artificial agents and designing an AI system by instilling moral values in it.

Suggested Citation

  • Yulia Sullivan & Marc Bourmont & Mary Dunaway, 2022. "Appraisals of harms and injustice trigger an eerie feeling that decreases trust in artificial intelligence systems," Annals of Operations Research, Springer, vol. 308(1), pages 525-548, January.
  • Handle: RePEc:spr:annopr:v:308:y:2022:i:1:d:10.1007_s10479-020-03702-9
    DOI: 10.1007/s10479-020-03702-9
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    1. Bernard Han & Jack Cook, 1998. "An efficient heuristic for robot acquisition and cell formation," Annals of Operations Research, Springer, vol. 77(0), pages 229-252, January.
    2. J. Beck & Barbara Smith, 2009. "Introduction to the special volume on constraint programming, artificial intelligence, and operations research," Annals of Operations Research, Springer, vol. 171(1), pages 1-2, October.
    3. Edmond Awad & Sohan Dsouza & Richard Kim & Jonathan Schulz & Joseph Henrich & Azim Shariff & Jean-François Bonnefon & Iyad Rahwan, 2018. "The Moral Machine experiment," Nature, Nature, vol. 563(7729), pages 59-64, November.
    4. Sanja Petrovic, 2019. "“You have to get wet to learn how to swim” applied to bridging the gap between research into personnel scheduling and its implementation in practice," Annals of Operations Research, Springer, vol. 275(1), pages 161-179, April.
    5. Paul A. Pavlou & David Gefen, 2005. "Psychological Contract Violation in Online Marketplaces: Antecedents, Consequences, and Moderating Role," Information Systems Research, INFORMS, vol. 16(4), pages 372-399, December.
    6. Vladimir Kats & Eugene Levner, 1997. "Minimizing the number of robots to meet a given cyclic schedule," Annals of Operations Research, Springer, vol. 69(0), pages 209-226, January.
    7. Peter Brucker & Sigrid Knust, 2002. "Lower Bounds for Scheduling a Single Robot in a Job-Shop Environment," Annals of Operations Research, Springer, vol. 115(1), pages 147-172, September.
    8. El-Ghazali Talbi, 2016. "Combining metaheuristics with mathematical programming, constraint programming and machine learning," Annals of Operations Research, Springer, vol. 240(1), pages 171-215, May.
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    1. Omrani, Nessrine & Rivieccio, Giorgia & Fiore, Ugo & Schiavone, Francesco & Agreda, Sergio Garcia, 2022. "To trust or not to trust? An assessment of trust in AI-based systems: Concerns, ethics and contexts," Technological Forecasting and Social Change, Elsevier, vol. 181(C).

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