Artificial Intelligence and the Mobilities of Inclusion: The Accumulated Advantages of 5G Networks and Surfacing Outliers
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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.
KeywordsAccumulated advantage Artificial intelligence ICT4D Digital divide Mobile learning 5G
- Azhar, A. (2016). Coding is not enough, we need smarter skills. Financial Times. https://www.ft.com/content/7babc12c-f662-11e5-96db-fc683b5e52db.
- Bebchuk, L. A. (2009). Pay without performance: The unfulfilled promise of executive compensation. Cambridge, MA: Harvard University Press.Google Scholar
- Bridge International Academies (2016). Model. Accessed January 22, 2016. http://www.bridgeinternationalacademies.com/approach/model/.
- Bridge International Academies. (2018). Teaching tools. Accessed July 13, 2018. http://www.bridgeinternationalacademies.com/academics/tools/.
- Dignum, V. (2018). Designing AI for human values. ITU Journal, 1(1). Available at: https://www.itu.int/en/journal/001/Pages/01.aspx.
- 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.
- Fenwick, T., Edwards, R., & Sawchuk, P. (2011). Emerging approaches to educational research: Tracing the sociomaterial. London: Routledge.Google Scholar
- 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
- 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
- Goggin, G. (2012). Cell phone culture: Mobile technology in everyday life. London: Routledge.Google Scholar
- GPPP. (2014). The 5G infrastructure public-private partnership. Available at: https://5g-ppp.eu/.
- 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
- GSMA. (2018a). The mobile economy 2018. Available at: https://www.gsma.com/mobileeconomy/wp-content/uploads/2018/02/The-Mobile-Economy-Global-2018.pdf.
- 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.
- Hesse-Biber, S. N. (Ed.). (2011). The handbook of emergent technologies in social research. Oxford: Oxford University Press.Google Scholar
- International Telecommunication Union (ITU). (2017). ICT facts and figures 2017. Available at: https://www.itu.int/en/ITU-D/Statistics/Documents/facts/ICTFactsFigures2017.pdf.
- Joh, E. E. (2018). Artificial intelligence and policing: First questions. Available at: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3168779.
- 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
- Lefebvre, H. (2004). Rythmanalaysis: space, time and everyday life. London: Continuum.Google Scholar
- Mansell, R. (2017). Are we losing control? Intermedia, 45(3), 4–7.Google Scholar
- 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.
- 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
- 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
- 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
- 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
- Sangam, P. (2018). Living on the wireless edge with AI And 5G. Available: https://www.forbes.com/sites/forbescommunicationscouncil/2018/09/06/living-on-the-wireless-edge-with-ai-and-5g/#71cc8de6b6b4.
- Star, S. L. (1998). 13 Working together: Symbolic interactionism, activity theory, and information systems. Cognition and communication at work, 296.Google Scholar
- 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.
- We Are Social. (2018). Digital Report 2018. Available at: https://digitalreport.wearesocial.com/.