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Speciesism: an obstacle to AI and robot adoption

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  • Bernd Schmitt

    (Columbia University)

Abstract

Once artificial intelligence (AI) is indistinguishable from human intelligence, and robots are highly similar in appearance and behavior to humans, there should be no reason to treat AI and robots differently from humans. However, even perfect AI and robots may still be subject to a bias (referred to as speciesism in this article), which will disadvantage them and be a barrier to their commercial adoption as chatbots, decision and recommendation systems, and staff in retail and service settings. The author calls for future research that determines causes and psychological consequences of speciesism, assesses the effect of speciesism on the adoption of new products and technologies, and identifies ways to overcome it.

Suggested Citation

  • Bernd Schmitt, 2020. "Speciesism: an obstacle to AI and robot adoption," Marketing Letters, Springer, vol. 31(1), pages 3-6, March.
    • Handle: RePEc:kap:mktlet:v:31:y:2020:i:1:d:10.1007_s11002-019-09499-3
    DOI: 10.1007/s11002-019-09499-3
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    References listed on IDEAS

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    1. Nicholas Epley, 2018. "A Mind like Mine: The Exceptionally Ordinary Underpinnings of Anthropomorphism," Journal of the Association for Consumer Research, University of Chicago Press, vol. 3(4), pages 591-598.
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    Cited by:

    1. Benjamin von Walter & Dietmar Kremmel & Bruno Jäger, 2022. "The impact of lay beliefs about AI on adoption of algorithmic advice," Marketing Letters, Springer, vol. 33(1), pages 143-155, March.
    2. Martin Mende & Stephanie M. Noble & Thomas Sugar, 2023. "From homo sapiens to homo superior? Wearable robotics as the platform for transhumanist marketing," Journal of the Academy of Marketing Science, Springer, vol. 51(4), pages 757-766, July.
    3. Yuping Liu-Thompkins & Shintaro Okazaki & Hairong Li, 2022. "Artificial empathy in marketing interactions: Bridging the human-AI gap in affective and social customer experience," Journal of the Academy of Marketing Science, Springer, vol. 50(6), pages 1198-1218, November.
    4. Kamoonpuri, Sana Zehra & Sengar, Anita, 2023. "Hi, May AI help you? An analysis of the barriers impeding the implementation and use of artificial intelligence-enabled virtual assistants in retail," Journal of Retailing and Consumer Services, Elsevier, vol. 72(C).
    5. Chen, Nuoya & Mohanty, Smaraki & Jiao, Jinfeng & Fan, Xiucheng, 2021. "To err is human: Tolerate humans instead of machines in service failure," Journal of Retailing and Consumer Services, Elsevier, vol. 59(C).
    6. Vlačić, Božidar & Corbo, Leonardo & Costa e Silva, Susana & Dabić, Marina, 2021. "The evolving role of artificial intelligence in marketing: A review and research agenda," Journal of Business Research, Elsevier, vol. 128(C), pages 187-203.
    7. Alabed, Amani & Javornik, Ana & Gregory-Smith, Diana, 2022. "AI anthropomorphism and its effect on users' self-congruence and self–AI integration: A theoretical framework and research agenda," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    8. Aric Rindfleisch & Nobuyuki Fukawa & Naoto Onzo, 2022. "Robots in retail: Rolling out the Whiz," AMS Review, Springer;Academy of Marketing Science, vol. 12(3), pages 238-244, December.
    9. Zhu, Yimin & Zhang, Jiemin & Wu, Jifei & Liu, Yingyue, 2022. "AI is better when I'm sure: The influence of certainty of needs on consumers' acceptance of AI chatbots," Journal of Business Research, Elsevier, vol. 150(C), pages 642-652.
    10. Frank, Darius-Aurel & Otterbring, Tobias, 2023. "Being seen… by human or machine? Acknowledgment effects on customer responses differ between human and robotic service workers," Technological Forecasting and Social Change, Elsevier, vol. 189(C).

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