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Artificial Intelligence and the Mobilities of Inclusion: The Accumulated Advantages of 5G Networks and Surfacing Outliers

  • Michael GallagherEmail author
Chapter
Part of the Perspectives on Rethinking and Reforming Education book series (PRRE)

Abstract

The use of artificial intelligence in a learning increasingly mediated through mobile technology makes inclusion problematic. This is due to the ubiquity of mobile technology, the complexity of the machine learning regimens needed to function within increasingly sophisticated 5G cellular networks, and the legions of professionals needed to initiate and maintain these AI and mobile ecosystems. The promise of artificial intelligence in inclusion is curtailed due to the accumulated advantage (the Matthew effect) presented in such a technological sophistication: only those with the most sophisticated of educational systems will stand to benefit, a scenario that poses significant impact on inclusion strategies increasingly mediated through ICT. Inclusion operates as an outlier in these data-driven environments: as an equitable model in education, it is designed to counter prevailing societal biases, rather than conforming to them. As more and more education is engaged through mobile technology and more and more of that mobile education is driven by an artificial intelligence emerging from curricula of greater and greater sophistication, a situation emerges that poses great challenges for any sort of meaningful inclusion, particularly in the potential acceleration of entrenched advantage. This chapter explores the problematic intersections of AI, mobile technology, and inclusion.

Keywords

Accumulated advantage Artificial intelligence ICT4D Digital divide Mobile learning 5G 

References

  1. Aaronson, S. A., & Leblond, P. (2018). Another digital divide: The rise of data realms and its implications for the WTO. Journal of International Economic Law, 21(2), 245–272.CrossRefGoogle Scholar
  2. Antonelli, C., & Crespi, F. (2013). The “Matthew effect” in R&D public subsidies: The Italian evidence. Technological Forecasting and Social Change, 80(8), 1523–1534.CrossRefGoogle Scholar
  3. Azhar, A. (2016). Coding is not enough, we need smarter skills. Financial Times. https://www.ft.com/content/7babc12c-f662-11e5-96db-fc683b5e52db.
  4. Bayne, S., Gallagher, M. S., & Lamb, J. (2014). Being ‘at’ university: The social topologies of distance students. Higher Education, 67(5), 569–583.CrossRefGoogle Scholar
  5. Bebchuk, L. A. (2009). Pay without performance: The unfulfilled promise of executive compensation. Cambridge, MA: Harvard University Press.Google Scholar
  6. Bothner, M. S., Haynes, R., Lee, W., & Smith, E. B. (2010). When do Matthew effects occur? Journal of Mathematical Sociology, 34, 80–114.CrossRefGoogle Scholar
  7. Bridge International Academies (2016). Model. Accessed January 22, 2016. http://www.bridgeinternationalacademies.com/approach/model/.
  8. Bridge International Academies. (2018). Teaching tools. Accessed July 13, 2018. http://www.bridgeinternationalacademies.com/academics/tools/.
  9. Caliskan, A., Bryson, J. J., & Narayanan, A. (2017). Semantics derived automatically from language corpora contain human-like biases. Science, 356, 183–186.CrossRefGoogle Scholar
  10. Chouliaraki, L. (2012). Cosmopolitanism as irony: a critique of post-humanitarianism. In After cosmopolitanism (pp. 87–106). London: Routledge.CrossRefGoogle Scholar
  11. Dignum, V. (2018). Designing AI for human values. ITU Journal, 1(1). Available at: https://www.itu.int/en/journal/001/Pages/01.aspx.
  12. Ericsson. (2018). Future mobile data usage and traffic growth. Available at: https://www.ericsson.com/en/mobility-report/future-mobile-data-usage-and-traffic-growth.
  13. Fenwick, T., Edwards, R., & Sawchuk, P. (2011). Emerging approaches to educational research: Tracing the sociomaterial. London: Routledge.Google Scholar
  14. Fortunati, L., & Taipale, S. (2017). Mobilities and the network of personal technologies: Refining the understanding of mobility structure. Telematics and Informatics, 34(2), 560–568.CrossRefGoogle Scholar
  15. Gallagher, M. (2019 Forthcoming). Moving beyond microwork: Rebundling digital education and reterritorialising digital labour. In M. A. Peters, P. Jandrić, & A. J. Means (Eds.), Education and technological unemployment. Berlin: Springer.Google Scholar
  16. Gergen, K. J. (2003). Self and community in the new floating worlds. In K. Nyiri (Ed.), Mobile democracy: Essays on society, self, and politics. Vienna: Passagen Verlag.Google Scholar
  17. Goggin, G. (2012). Cell phone culture: Mobile technology in everyday life. London: Routledge.Google Scholar
  18. GPPP. (2014). The 5G infrastructure public-private partnership. Available at: https://5g-ppp.eu/.
  19. Graesser, A., & McDaniel, B. (2017). Conversational agents can provide formative assessment, constructive learning, and adaptive instruction. In The future of assessment (pp. 85–112). London: Routledge.Google Scholar
  20. GSMA. (2018b). A toolkit for researching women’s internet access and use. Available at: https://www.gsma.com/mobilefordevelopment/wp-content/uploads/2018/05/GSMA-Women-and-Internet-Research-Toolkit_WEB.pdf.
  21. Hannam, K., Sheller, M., & Urry, J. (2006). Editorial: Mobilities, immobilities and moorings. Mobilities, 1(1), 1–22.CrossRefGoogle Scholar
  22. Heeks, R., & Renken, J. (2018). Data justice for development: What would it mean? Information Development, 34(1), 90–102.CrossRefGoogle Scholar
  23. Hesse-Biber, S. N. (Ed.). (2011). The handbook of emergent technologies in social research. Oxford: Oxford University Press.Google Scholar
  24. International Telecommunication Union (ITU). (2017). ICT facts and figures 2017. Available at: https://www.itu.int/en/ITU-D/Statistics/Documents/facts/ICTFactsFigures2017.pdf.
  25. Joh, E. E. (2018). Artificial intelligence and policing: First questions. Available at: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3168779.
  26. Khetselius, O. Y., Glushkov, A. V., Buyadzhi, V. V., & Bunyakova, Y. Y. (2017). New generalized chaos-dynamical and neural networks approach to nonlinear modeling of the chaotic dynamical systems. Photoelectronics, 26, 29–40.CrossRefGoogle Scholar
  27. Lavery, M. P., Abadi, M. M., Bauer, R., Brambilla, G., Cheng, L., Cox, M. A., … & Marquardt, C. (2018). Tackling Africa’s digital divide. Nature Photonics, 12(5), 249–252.Google Scholar
  28. Lefebvre, H. (2004). Rythmanalaysis: space, time and everyday life. London: Continuum.Google Scholar
  29. Li, R., Zhao, Z., Zhou, X., Ding, G., Chen, Y., Wang, Z., et al. (2017). Intelligent 5G: When cellular networks meet artificial intelligence. IEEE Wireless Communications, 24(5), 175–183.CrossRefGoogle Scholar
  30. Mansell, R. (2017). Are we losing control? Intermedia, 45(3), 4–7.Google Scholar
  31. McVeigh, K., & Lyons, K. (2017, May 5). ‘Beyond justification’: teachers decry UK backing for private schools in Africa. The Guardian. https://www.theguardian.com/global-development/2017/may/05/beyond-justification-teachers-decry-uk-backing-private-schools-africa-bridge-international-academies-kenya-lawsuit.
  32. Merton, R. K. (1968). The Matthew effect in science. Science, 159, 56–63.CrossRefGoogle Scholar
  33. Merton, R. (1988). The Matthew effect in science, II: Cumulative advantage and the symbolism of intellectual property. Isis, 79(4) (1988), 606–623.CrossRefGoogle Scholar
  34. Miller, F. A., Katz, J. H., & Gans, R. (2018). The OD imperative to add inclusion to the algorithms of artificial intelligence. OD PRACTITIONER, 50(1).Google Scholar
  35. Min, W., Frankosky, M. H., Mott, B. W., Wiebe, E. N., Boyer, K. E., & Lester, J. C. (2017, June). Inducing stealth assessors from game interaction data. In International Conference on Artificial Intelligence in Education (pp. 212–223). Springer, Cham.Google Scholar
  36. Perc, M. (2014). The Matthew effect in empirical data. Journal of the Royal Society, Interface, 11(98), 20140378.CrossRefGoogle Scholar
  37. Piezunka, H., Lee, W., Haynes, R., & Bothner, M. S. (2017). The Matthew effect as an unjust competitive advantage: Implications for competition near status boundaries. Journal of Management Inquiry.  https://doi.org/10.1177/1056492617737712.CrossRefGoogle Scholar
  38. Pype, JK. (2018). Mobile secrets. Youth, intimacy, and the politics of pretense in Mozambique by Julie-Soleil Archambault (review). African Studies Review, 61(1), 275–277.CrossRefGoogle Scholar
  39. Raizada, R. D., & Kishiyama, M. M. (2010). Effects of socioeconomic status on brain development, and how cognitive neuroscience may contribute to levelling the playing field. Frontiers in Human Neuroscience, 4, 3.Google Scholar
  40. Riep, C. B. (2017a). Fixing contradictions of education commercialisation: Pearson plc and the construction of its efficacy brand. Critical Studies in Education, 1–19.Google Scholar
  41. Riep, C. B. (2017b). Making markets for low-cost schooling: The devices and investments behind Bridge International Academies. Globalisation, Societies and Education, 15(3), 352–366.CrossRefGoogle Scholar
  42. Sheller, M., & Urry, J. (2006). The new mobilities paradigm. Environment and Planning A, 38, 207–226.CrossRefGoogle Scholar
  43. Sheller, M., & Urry, J. (2016). Mobilizing the new mobilities paradigm. Applied Mobilities, 1(1), 10–25.CrossRefGoogle Scholar
  44. Sinha, S. (2018). Gender digital divide in India: Impacting women’s participation in the labour market. In Reflecting on India’s development (pp. 293–310). Singapore: Springer.CrossRefGoogle Scholar
  45. Srivastava, L. (2005). Mobile phones and the evolution of social behavior. Behavior & Information Technology, 24(2005), 111–129.CrossRefGoogle Scholar
  46. Stanovich, K. E. (2008). Matthew effects in reading: some consequences of individual differences in the acquisition of literacy. Journal of Education, 189, 23–55.CrossRefGoogle Scholar
  47. Star, S. L. (1998). 13 Working together: Symbolic interactionism, activity theory, and information systems. Cognition and communication at work, 296.Google Scholar
  48. Star, S. L. (2010). This is not a boundary object: Reflections on the origin of a concept. Science, Technology and Human Values, 35(5), 601–617.CrossRefGoogle Scholar
  49. Taipale, S. (2016). Do the mobile-rich get richer? Internet use, travelling and social differentiations in Finland. New Media & Society, 18(1), 44–61.CrossRefGoogle Scholar
  50. Taylor, L. (2017). What is data justice? The case for connecting digital rights and freedoms globally. Big Data & Society, 4(2), 2053951717736335.CrossRefGoogle Scholar
  51. Teevan, J. (2016). The future of microwork. XRDS: Crossroads, The ACM Magazine for Students, 23(2), 26–29.CrossRefGoogle Scholar
  52. The AI Now Report. (2016, September 22). The social and economic implications of artificial intelligence technologies in the near-term. AI Now (Summary of public symposium). Available at: https://artificialintelligencenow.com/media/documents/AINowSummaryReport_3_RpmwKHu.pdf.
  53. Waterton, E., & Watson, S. (2013). Framing theory: Towards a critical imagination in heritage studies. International Journal of Heritage Studies, 19(6), 546–561.CrossRefGoogle Scholar
  54. We Are Social. (2018). Digital Report 2018. Available at: https://digitalreport.wearesocial.com/.
  55. Wenger, E. (1998). Communities of practice: Learning, meaning, and identity (learning in doing: social, cognitive and computational perspectives). Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  56. Whitty, G. (2017). The marketization of teacher education: Threat or opportunity? In A Companion to Research in Teacher Education (pp. 373–383). Singapore: Springer.CrossRefGoogle Scholar
  57. Yang, X., Gu, X., Wang, Y., Hu, G., & Tang, L. (2015). The Matthew effect in China’s science: Evidence from academicians of Chinese Academy of Sciences. Scientometrics, 102(3), 2089–2105.CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Centre for Research in Digital EducationUniversity of EdinburghEdinburghUK

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