Skip to main content
SpringerLink
Log in

What Makes AI Addictive? The Role of Discounting, Risk Aversion and Self-regulation

  • Conference paper
  • First Online:
Big Data Intelligence and Computing (DataCom 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13864))

Included in the following conference series:

  • 694 Accesses

Abstract

AI-enabled technology, with its capabilities of parsing large data sets and adaptively tuning its learning capabilities, has the potential to keep users “hooked”. However, this poses a problem for child users, since their ongoing cognitive development is not adequately primed to implement self-regulation. In this paper, we evaluate the impact of technology overuse by studying its impact on limited attention resources among children. We examine the factors that make AI-enabled technology addictive for children, specifically the impact of the short-term and long-term discounting tendencies and the degree of risk-aversion prevalent among child users. Our work in this paper illustrates the unique attributes of child users of technology, and therefore calls for technology design that can enhance the user experience of children by avoiding negative outcomes associated with the over-usage and addiction of AI-enabled technology.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 69.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 89.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Alter, A: Irresistible: the rise of addictive technology and the business of keeping us hooked. Penguin (2017)

    Google Scholar 

  2. Ammari, T., Kaye, J., Tsai, J.Y., Bentley, F.: Music, search, and IoT: how people (really) use voice assistants. ACM Trans. Comput. Hum. Interact. 26(3), 1–28 (2019)

    Article  Google Scholar 

  3. Arrow, K.: The theory of risk-bearing: small and great risks. J. Risk Uncertain. 12(2–3), 103–111 (1996)

    Article  Google Scholar 

  4. Augeraud-Veron, E., Bambi, M., Gozzi, F.: Solving internal habit formation models through dynamic programming in infinite dimension. J. Optim. Theory Appl. 173, 584–611 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  5. Berman, G., Albright, K.: Children and the data cycle: rights and ethics in a big data world. Innocenti Working Paper WP-2017-05, UNICEF Office of Research, Florence (2017)

    Google Scholar 

  6. Brand, M., Young, K., Laier, C.: Prefrontal control and internet addiction: a theoretical model and review of neuropsychological and neuroimaging findings. Front. Hum. Neurosci. 8, 375 (2014)

    Article  Google Scholar 

  7. Bossi, L., Gomis-Porqueras, P.: Consequences of modeling habit persistence. Macroecon. Dyn. 13(3), 349–365 (2009)

    Article  MATH  Google Scholar 

  8. Burnay, J., Billieux, J., Blairy, S., Laroi, F.: Which psychological factors influence Internet addiction? Evidence through an integrative model. Comput. Hum. Behav. 43, 28–34 (2015)

    Article  Google Scholar 

  9. Carr, N.: The Shallows: What the Internet is Doing to Our Brains. WW Norton & Company (2011)

    Google Scholar 

  10. Druin, A.: The role of children in the design of new technology. Behav. Inf. Technol. 21(1), 1–25 (2002)

    Article  Google Scholar 

  11. Gomes, F., Michaelides, A.: Portfolio choice with internal habit formation: a life-cycle model with uninsurable labor income risk. Rev. Econ. Dyn. 6(4), 729–766 (2003)

    Article  Google Scholar 

  12. Hadlington, L., White, H., Curtis, S.: “I cannot live without my [tablet]”: children’s experiences of using tablet technology within the home. Comput. Hum. Behav. 94, 19–24 (2019)

    Article  Google Scholar 

  13. Hughes, J., Merges, R.: Copyright and distributive justice. Notre Dame L. Rev. 92, 513 (2016)

    Google Scholar 

  14. Iskender, M., Akin, A.: Social self-efficacy, academic locus of control, and internet addiction. Comput. Educ. 54(4), 1101–1106 (2010)

    Article  Google Scholar 

  15. Ito, M., et al.: Living and Learning with New Media: Summary of Findings from the Digital Youth Project. John D. and Catherine T. MacArthur Foundation (2008)

    Google Scholar 

  16. Kaplow, L., Shavell, S.: Fairness versus welfare: notes on the Pareto principle, preferences, and distributive justice. J. Leg. Stud. 32(1), 331–362 (2003)

    Article  Google Scholar 

  17. Kaswan, A.: Distributive justice and the environment. NCL Rev. 81, 1031 (2002)

    Google Scholar 

  18. Konow, J.: Fair and square: the four sides of distributive justice. J. Econ. Behav. Organ. 46(2), 137–164 (2001)

    Article  Google Scholar 

  19. Leung, L.: Predicting Internet risks: a longitudinal panel study of gratifications-sought, Internet addiction symptoms, and social media use among children and adolescents. Health Psychol. Behav. Med. 2(1), 424–439 (2014)

    Article  Google Scholar 

  20. Leung, L., Lee, P.: Impact of internet literacy, internet addiction symptoms, and internet activities on academic performance. Soc. Sci. Comput. Rev. 30(4), 403–418 (2012)

    Article  MathSciNet  Google Scholar 

  21. Livingstone, S., Byrne, J., Carr, J.: One in Three: Internet Governance and Children’s Rights, Innocenti Discussion Papers no. 2016–01, UNICEF Office of Research - Innocenti, Florence (2016)

    Google Scholar 

  22. Mischel, W., Ebbesen, E.: Attention in delay of gratification. J. Pers. Soc. Psychol. 16(2), 329 (1970)

    Article  Google Scholar 

  23. Mosher, D.: High wired: does addictive Internet use restructure the brain? Scientific American. https://www.scientificamerican.com/article/does-addictive-internet-use-restructure-brain/. Accessed 29 Apr 2022

  24. Naryshkin, R., Davison, M.: Developing utility functions for optimal consumption in models with habit formation and catching up with the Joneses. Can. Appl. Math. Q. 17(4), 703–719 (2009)

    MathSciNet  MATH  Google Scholar 

  25. Perali, F., Piccoli, L., Wangen, K.: An extended theory of rational addiction (No. 69). Universitat de les Illes Balears, Departament d’Economía Aplicada (2015)

    Google Scholar 

  26. Pew Research Center – Internet and Technology Report. Many turn to youtube for children’s content, news, how-to lessons. https://www.pewresearch.org/topic/internet-technology/. Accessed 29 Apr 2022

  27. Plowman, L., McPake, J., Stephen, C.: The technologisation of childhood? Young children and technology in the home. Child. Soc. 24(1), 63–74 (2010)

    Article  Google Scholar 

  28. Reyna, V., Farley, F.: Risk and rationality in adolescent decision making: implications for theory, practice, and public policy. Psychol. Sci. Public Interest 7(1), 1–44 (2006)

    Article  Google Scholar 

  29. Roshan, R., Pahlavani, M., Shahiki, M.: Investigation on habit formation, risk aversion and intertemporal substitution in consumption of Iranian households by GMM approach. Int. Econ. Stud. 42(1), 47–56 (2013)

    Google Scholar 

  30. Singer, N.: Can’t put down your device? That’s by design. The New York Times. https://www.nytimes.com/2015/12/06/technology/personaltech/cant-put-down-your-device-thats-by-design.html. Accessed 29 Apr 2022

  31. Smahel, D., Wright, M.F., Cernikova, M.: The impact of digital media on health: children’s perspectives. Int. J. Public Health 60(2), 131–137 (2015). https://doi.org/10.1007/s00038-015-0649-z

    Article  Google Scholar 

  32. Stern, S.E.: Addiction to technologies: a social psychological perspective of Internet addiction. Cyberpsychol. Behav. 2(5), 419–424 (1999)

    Article  Google Scholar 

  33. Yayouk, W., et al.: Genetic and environmental influences on self-control: assessing self-control with the ASEBA self-control scale. Behav. Genet. 48(2), 135–146 (2018)

    Article  Google Scholar 

  34. Young, K.: Internet addiction: the emergence of a new clinical disorder. Cyberpsychol. Behav. 1(3), 237–244 (1998)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Dallas, Irving, TX, 75062, USA

    Renita Murimi

Authors
  1. Renita Murimi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Renita Murimi .

Editor information

Editors and Affiliations

  1. Asia University, Taichung, Taiwan

    Ching-Hsien Hsu

  2. Beijing University of Posts and Telecommunications, Beijing, China

    Mengwei Xu

  3. University of New Brunswick, Fredericton, NB, Canada

    Hung Cao

  4. Asia University, Taichung, Taiwan

    Hojjat Baghban

  5. University of Fiji, Samabula, Fiji

    A. B. M. Shawkat Ali

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Murimi, R. (2023). What Makes AI Addictive? The Role of Discounting, Risk Aversion and Self-regulation. In: Hsu, CH., Xu, M., Cao, H., Baghban, H., Shawkat Ali, A.B.M. (eds) Big Data Intelligence and Computing. DataCom 2022. Lecture Notes in Computer Science, vol 13864. Springer, Singapore. https://doi.org/10.1007/978-981-99-2233-8_32

Download citation

  • DOI: https://doi.org/10.1007/978-981-99-2233-8_32

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-2232-1

  • Online ISBN: 978-981-99-2233-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation