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The Role of Consumer Autonomy in Developing Sustainable AI: A Conceptual Framework

Author

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  • Lena Bjørlo

    (Department of International Business, Norwegian University of Science and Technology (NTNU), 6009 Ålesund, Norway)

  • Øystein Moen

    (Department of Industrial Economics and Technology Management, Norwegian University of Science and Technology (NTNU), 7491 Trondheim, Norway)

  • Mark Pasquine

    (Department of International Business, Norwegian University of Science and Technology (NTNU), 6009 Ålesund, Norway)

Abstract

Artificial intelligence (AI)-based decision aids are increasingly employed by businesses to assist consumers’ decision-making. Personalized content based on consumers’ data brings benefits for both consumers and businesses, i.e., with regards to more relevant content. However, this practice simultaneously enables increased possibilities for exerting hidden interference and manipulation on consumers, reducing consumer autonomy. We argue that due to this, consumer autonomy represents a resource at the risk of depletion and requiring protection, due to its fundamental significance for a democratic society. By balancing advantages and disadvantages of increased influence by AI, this paper addresses an important research gap and explores the essential challenges related to the use of AI for consumers’ decision-making and autonomy, grounded in extant literature. We offer a constructive, rather than optimistic or pessimistic, outlook on AI. Hereunder, we present propositions suggesting how these problems may be alleviated, and how consumer autonomy may be protected. These propositions constitute the fundament for a framework regarding the development of sustainable AI, in the context of online decision-making. We argue that notions of transparency, complementarity, and privacy regulation are vital for increasing consumer autonomy and promoting sustainable AI. Lastly, the paper offers a definition of sustainable AI within the contextual boundaries of online decision-making. Altogether, we position this paper as a contribution to the discussion of development towards a more socially sustainable and ethical use of AI.

Suggested Citation

  • Lena Bjørlo & Øystein Moen & Mark Pasquine, 2021. "The Role of Consumer Autonomy in Developing Sustainable AI: A Conceptual Framework," Sustainability, MDPI, vol. 13(4), pages 1-18, February.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:4:p:2332-:d:503198
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    References listed on IDEAS

    as
    1. Bleier, Alexander & Eisenbeiss, Maik, 2015. "The Importance of Trust for Personalized Online Advertising," Journal of Retailing, Elsevier, vol. 91(3), pages 390-409.
    2. Ajay Agrawal & Joshua Gans & Avi Goldfarb, 2019. "Economic Policy for Artificial Intelligence," Innovation Policy and the Economy, University of Chicago Press, vol. 19(1), pages 139-159.
    3. World Commission on Environment and Development,, 1987. "Our Common Future," OUP Catalogue, Oxford University Press, number 9780192820808.
    4. Pfeffer, Jeffrey, 2010. "Building Sustainable Organizations: The Human Factor," Research Papers 2017r, Stanford University, Graduate School of Business.
    5. Virgilio Gilart-Iglesias & Higinio Mora & Raquel Pérez-delHoyo & Clara García-Mayor, 2015. "A Computational Method based on Radio Frequency Technologies for the Analysis of Accessibility of Disabled People in Sustainable Cities," Sustainability, MDPI, vol. 7(11), pages 1-29, November.
    6. Grewal, Dhruv & Roggeveen, Anne L. & Nordfält, Jens, 2017. "The Future of Retailing," Journal of Retailing, Elsevier, vol. 93(1), pages 1-6.
    7. Alexandros Nikitas & Kalliopi Michalakopoulou & Eric Tchouamou Njoya & Dimitris Karampatzakis, 2020. "Artificial Intelligence, Transport and the Smart City: Definitions and Dimensions of a New Mobility Era," Sustainability, MDPI, vol. 12(7), pages 1-19, April.
    8. Quentin André & Ziv Carmon & Klaus Wertenbroch & Alia Crum & Douglas Frank & William Goldstein & Joel Huber & Leaf Boven & Bernd Weber & Haiyang Yang, 2018. "Consumer Choice and Autonomy in the Age of Artificial Intelligence and Big Data," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 5(1), pages 28-37, March.
    9. Avi Goldfarb & Catherine E. Tucker, 2011. "Privacy Regulation and Online Advertising," Management Science, INFORMS, vol. 57(1), pages 57-71, January.
    10. Tan Yigitcanlar & Federico Cugurullo, 2020. "The Sustainability of Artificial Intelligence: An Urbanistic Viewpoint from the Lens of Smart and Sustainable Cities," Sustainability, MDPI, vol. 12(20), pages 1-24, October.
    11. De Bruyn, Arnaud & Lilien, Gary L., 2008. "A multi-stage model of word-of-mouth influence through viral marketing," International Journal of Research in Marketing, Elsevier, vol. 25(3), pages 151-163.
    12. Kannan, P.K. & Li, Hongshuang “Alice”, 2017. "Digital marketing: A framework, review and research agenda," International Journal of Research in Marketing, Elsevier, vol. 34(1), pages 22-45.
    13. Vasile Gherheș & Ciprian Obrad, 2018. "Technical and Humanities Students’ Perspectives on the Development and Sustainability of Artificial Intelligence (AI)," Sustainability, MDPI, vol. 10(9), pages 1-16, August.
    14. Susser, Daniel & Roessler, Beate & Nissenbaum, Helen, 2019. "Technology, autonomy, and manipulation," Internet Policy Review: Journal on Internet Regulation, Alexander von Humboldt Institute for Internet and Society (HIIG), Berlin, vol. 8(2), pages 1-22.
    15. Bleier, Alexander & Goldfarb, Avi & Tucker, Catherine, 2020. "Consumer privacy and the future of data-based innovation and marketing," International Journal of Research in Marketing, Elsevier, vol. 37(3), pages 466-480.
    16. Bettman, James R & Luce, Mary Frances & Payne, John W, 1998. "Constructive Consumer Choice Processes," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 25(3), pages 187-217, December.
    17. Shugan, Steven M, 1980. "The Cost of Thinking," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 7(2), pages 99-111, Se.
    18. Lurie, Nicholas H. & Wen, Na, 2014. "Simple Decision Aids and Consumer Decision Making," Journal of Retailing, Elsevier, vol. 90(4), pages 511-523.
    19. Gavan J. Fitzsimons & Donald R. Lehmann, 2004. "Reactance to Recommendations: When Unsolicited Advice Yields Contrary Responses," Marketing Science, INFORMS, vol. 23(1), pages 82-94, September.
    20. Berkeley J. Dietvorst & Joseph P. Simmons & Cade Massey, 2018. "Overcoming Algorithm Aversion: People Will Use Imperfect Algorithms If They Can (Even Slightly) Modify Them," Management Science, INFORMS, vol. 64(3), pages 1155-1170, March.
    21. Ricardo Vinuesa & Hossein Azizpour & Iolanda Leite & Madeline Balaam & Virginia Dignum & Sami Domisch & Anna Felländer & Simone Daniela Langhans & Max Tegmark & Francesco Fuso Nerini, 2020. "The role of artificial intelligence in achieving the Sustainable Development Goals," Nature Communications, Nature, vol. 11(1), pages 1-10, December.
    22. Chernev, Alexander, 2003. "When More Is Less and Less Is More: The Role of Ideal Point Availability and Assortment in Consumer Choice," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 30(2), pages 170-183, September.
    23. Bremer, Lucas & Heitmann, Mark & Schreiner, Thomas F., 2017. "When and how to infer heuristic consideration set rules of consumers," International Journal of Research in Marketing, Elsevier, vol. 34(2), pages 516-535.
    24. Campbell, Colin & Sands, Sean & Ferraro, Carla & Tsao, Hsiu-Yuan (Jody) & Mavrommatis, Alexis, 2020. "From data to action: How marketers can leverage AI," Business Horizons, Elsevier, vol. 63(2), pages 227-243.
    25. Rust, Roland T., 2020. "The future of marketing," International Journal of Research in Marketing, Elsevier, vol. 37(1), pages 15-26.
    26. Gerald Häubl & Valerie Trifts, 2000. "Consumer Decision Making in Online Shopping Environments: The Effects of Interactive Decision Aids," Marketing Science, INFORMS, vol. 19(1), pages 4-21, May.
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    Cited by:

    1. Wilson, Christopher & van der Velden, Maja, 2022. "Sustainable AI: An integrated model to guide public sector decision-making," Technology in Society, Elsevier, vol. 68(C).
    2. Tan Yigitcanlar & Rashid Mehmood & Juan M. Corchado, 2021. "Green Artificial Intelligence: Towards an Efficient, Sustainable and Equitable Technology for Smart Cities and Futures," Sustainability, MDPI, vol. 13(16), pages 1-14, August.
    3. Carmen Isensee & Kai-Michael Griese & Frank Teuteberg, 2021. "Sustainable artificial intelligence: A corporate culture perspective [Sustainable artificial intelligence: Eine unternehmenskulturelle Perspektive]," Sustainability Nexus Forum, Springer, vol. 29(3), pages 217-230, December.

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