AI ecosystems; AI readiness; Digital transformation; Global South; Artificial intelligence ecosystem; Artificial intelligence readiness; Cambodia; Catching-up; Global south; Growing demand; Industrialisation; Innovative product; Senegal; Management Information Systems; Information Systems; Computer Networks and Communications; Marketing; Information Systems and Management; Library and Information Sciences; Artificial Intelligence
Abstract :
[en] The Global South (GS) refers to a block of countries in the process of rapidly catching up with the industrialization of their economies. GS countries are trying to leverage their growing demand for innovative products and services to negotiate the uptake of novel technologies such as Artificial Intelligence (AI). The latter can help such countries further stimulate their economic growth and become influential in dictating policies that could advance global development. However, a misaligned AI-adoption strategy entails the risk of alienating the social, economic, and even cultural requirements of such countries, ultimately leading to the incorporation of technologies not serving the needs of their users. Subsequently, we conduct a study whose goal is to provide guidelines on how to perform a comprehensive analysis of the AI ecosystems in GS countries, to determine their readiness to adopt and utilize beneficially these technologies. For this, we performed a qualitative case study of two GS countries, namely Senegal and Cambodia. The depiction of their AI ecosystems unveiled the need to stimulate their societies (i.e., by media exposure, education, etc.) in discerning the practical effects of AI. However, an impactful AI implementation requires a reciprocated satisfaction of the needs of multiple contributing actors. Finally, particular recommendations are offered at the level of these countries’ academic, industrial, and governmental constitutions, suggesting to focalize their resources in order to better energize (and optimally disperse) their research and entrepreneurial capabilities that could help identify the ways in which AI solutions can be of added-value within the premises of these two countries.
Disciplines :
Management information systems
Author, co-author :
Heng, Samedi ; Université de Liège - ULiège > HEC Liège : UER > UER Opérations : Digital Business
Tsilionis, Konstantinos; Faculty of Economics and Business, KU Leuven, Brussels, Belgium
Scharff, Christelle; Seidenberg School of CSIS, Pace University, New York, United States
Wautelet, Yves; Faculty of Economics and Business, KU Leuven, Brussels, Belgium
Language :
English
Title :
Understanding AI ecosystems in the Global South: The cases of Senegal and Cambodia
Interestingly, our interviewees have also placed a great importance on the role of NGO within these two countries when it comes to providing STEM awareness to society; this seems odd at first glance as we would have expected that goal to be maintained by Academia/Research Center. However, the interviewees emphasized on the dependency of the latter upon the (Senegalese/Cambodian) Government when it comes to the provision of funds making academia inhibited when it comes to determining their own educational curricula, and perhaps even the forthcoming focus-areas in research. This indirect reliance of academia on government is more intense in the case of Cambodia. This is easily visible in Fig. 2 where the Cambodian Academia/Research Center cannot access financial support from the Regional Organization (i.e., ASEAN); by contrast, the funding root of the Regional Organization is accessible to the Senegalese Academia/Research Center. This (phenomenally insignificant) missing link in the case of Cambodia has direct consequences in terms of the independence of the academic world and its role as a societal instigator/pedagogue.
Acemoglu, D., Introduction to economic growth. Journal of Economic Theory 147:2 (2012), 545–550.
Agarwal, N., & Brem, A. (2012, June). Frugal and reverse innovation-literature overview and case study insights from a German MNC in India and China. In 2012 18th international ICE conference on engineering, technology and innovation (pp. 1–11). IEEE.
Aleksander, I., Partners of humans: A realistic assessment of the role of robots in the ⦸foreseeable future. Journal of Information Technology 32:1 (2017), 1–9.
Alsheibani, S., Cheung, Y., & Messom, C. (2018). Artificial intelligence adoption: AI-readiness at firm-level. In PACIS (p. 37).
Ananny, M., Toward an ethics of algorithms: convening, observation, probability, and timeliness. Science, Technology, & Human Values 41:1 (2015), 93–117.
Arulkumaran, K., Deisenroth, M.P., Brundage, M., Bharath, A.A., A brief survey of deep reinforcement learning. IEEE Signal Processing Magazine 34:6 (2017), 26–38.
Atlam, H., Walters, R., & Wills, G. (2018). Intelligence of things: Opportunities & challenges. In IEEE 3rd cloudification of the internet of things (CIoT), Paris.
Borges, A.F.S., Laurindo, F.J.B., Spínola, M.M., Gonçalves, R.F., Mattos, C.A., The strategic use of artificial intelligence in the digital era: Systematic literature review and future research directions. International Journal of Information Management, 2020, 102225.
Brown, T.A., Confirmatory factor analysis for applied research. New York: Guilford. Organizational Research Methods 13:1 (2006), 214–217.
Bruckner, M., LaFleur, M., Pitterle, I., Frontier issues: The impact of the technological revolution on labor markets and income distribution. [Study]. 2017, Department of Economic and Social Affairs, UN, New York, NY, US 〈https://www.un.org/development/desa/dpad/wp-content/uploads/sites/45/publication/2017_Aug_Frontier-Issues-1.pdf〉.
Brynjolfsson, E., Rock, D., Syverson, C., Artificial intelligence and the modern productivity paradox: A clash of expectations and statistics. 2019, University of Chicago Press, 23–60.
Casarrubias, C. (2020). Fair Tech in the Global South: Realities and perceptions: Access partnership: Defining a path to Fair Tech. July 7, 2020. Retrieved from https://www.accesspartnership.com/pathtofairtech.
Das, S., Dey, A., Pal, A., Nabamita, R., Applications of artificial intelligence in machine learning: Review and prospect. International Journal of Computer Applications 115:9 (2015), 31–41.
Degli Esposti, S., When big data meets dataveillance: The hidden side of analytics. Surveillance & Society 12:2 (2014), 209–225.
Duan, Y., Edwards, J.S., Dwivedi, Y.K., Artificial intelligence for decision making in the era of Big Data–Evolution, challenges and research agenda. International Journal of Information Management 48 (2019), 63–71.
Dwivedi, Y.K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Williams, M.D., Artificial intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 2019, 101994.
Eli-Chukwu, N.C., Applications of artificial intelligence in agriculture: A review. Engineering, Technology & Applied Science Research 9:4 (2019), 4377–4383.
Ellefsen, A.P.T., Oleśków-Szłapka, J., Pawłowski, G., Toboła, A., Striving for excellence in AI implementation: AI maturity model framework and preliminary research results. LogForum, 2019, 15.
ESCAP. (2020). Artificial intelligence in Asia and the Pacific. Retrieved from https://www.unescap.org/sites/default/files/ESCAP_Artificial_Intelligence.pdf.
Fejerskov, A.M., The new technopolitics of development and the global south as a laboratory of technological experimentation. Science, Technology, & Human Values 42:5 (2017), 947–968.
Ford, M., The rise of the robots: Technology and the threat of mass unemployment. 2017, Oneworld Publications, London.
Frick, N.R., Mirbabaie, M., Stieglitz, S., Salomon, J., Maneuvering through the stormy seas of digital transformation: The impact of empowering leadership on the AI readiness of enterprises. Journal of Decision Systems, 2021, 1–24.
Garcia, M., Racist in the machine: The disturbing implications of algorithmic bias. World Policy Journal 33:4 (2017), 111–117.
Goodman, B., Flaxman, S., European union regulations on algorithmic decision-making and a ‘right to explanation’. AI Magazine 38:3 (2017), 50–57.
GSMA. (2000). Artificial intelligence and start-ups in low- and middle-income countries: Progress, promises and perils. Retrieved from https://www.gsma.com/mobilefordevelopment/wp-content/uploads/2020/10/Artificial-Intelligence-and-Start-Ups-in-Low-and-Middle-Income-Countries-Progress-Promises-Perils-Final.pdf.
Heng, P. (2019). Preparing Cambodia's workforce for a digital economy. Konrad-Adenauer-Stiftung, Cambodia. Retrieved from https://www.kas.de/documents/264850/264899/Preparing+Cambodia%C2%B4s+Workforce+for+a+Digital+Economy.pdf.
Henning, R., Donahue, N., Brand, M., End market research toolkit: Upgrading value chain competitiveness with informed choice. 2008, USAID-AMAP-BDS, Washington DC.
Hernández-Orallo, J., Martínez-Plumed, F., Avin, S., & Whittlestone, J. (2020). AI paradigms and AI safety: Mapping artefacts and techniques to safety issues. In Proceedings of the 24th European conference on artificial intelligence (pp. 2521–2528).
Hoekman, B. M., Maskus, K. E., & Saggi, K. (2004). Transfer of technology to developing countries: Unilateral and multilateral policy options. Policy Research Working Paper; No. 3332. World Bank, Washington, D.C. Retrieved from https://openknowledge.worldbank.org/handle/10986/14181.
Holmström, J., From AI to digital transformation: The AI readiness framework. Business Horizons, 2021.
International Institute of Communications. (2020). Artificial Intelligence in the Asia-Pacific Region Examining policies and strategies to maximise AI readiness and adoption. Retrieved from https://www.iicom.org/wp-content/uploads/IIC-AI-Report-2020.pdf.
Introna, L., Algorithms, governance and governmentality: On governing academic writing. Science, Technology and Human Values 41:1 (2013), 17–49.
Jöhnk, J., Weißert, M., Wyrtki, K., Ready or not, AI comes - An interview study of organizational AI readiness factors. Business & Information Systems Engineering 63:1 (2021), 5–20.
Kaplinsky, R., Schumacher meets Schumpeter: Appropriate technology below the radar. Research Policy 40:2 (2011), 193–203.
Kaul, I. (2013). The rise of the Global South: Implications for the provisioning of global public goods. UNDP-HDRO Occasional Papers, (2013/08).
Kolp, M., Faulkner, S., Wautelet, Y., Social structure based design patterns for agent-oriented software engineering. IJIIT 4:2 (2008), 1–23.
Li, J., Wang, L., Li, T., Zhu, S., Energy security pattern spatiotemporal evolution and strategic analysis of G20 countries. Sustainability, 11(6), 2019, 1629.
Lo, A., Dione, C. M. B., Nguer, E. M., Ba, S. O., Lo, M. (2020) Using LSTM to translate French to Senegalese local languages: Wolof as a case study. In Language resources and evaluation conference (LREC), 2020. Retrieved from https://arxiv.org/pdf/2004.13840.pdf.
Longhurst, R. (2009). Interviews: In-depth, semi-structured. In International encyclopedia of human geography (pp. 580–584). doi:10.1016/B978-008044910-4.00458-2.
Mack, N. (2005). Qualitative research methods: A data collector's field guide.
Martínez-Plumed, F., Gómez, E., Hernández-Orallo, J., Futures of artificial intelligence through technology readiness levels. Telematics and Informatics, 58, 2021, 101525.
Mbaye, O., Ba, M., Camara, G., Sy, A., Mboup, B., & Diallo, A. (2019). Towards an efficient prediction model of malaria cases in Senegal. In INTERSOL 2019.
McCarthy, J., Minsky, M., Rochester, N., & Shannon, C. E. (1955). A proposal for the Dartmouth Summer Research Project on Artificial Intelligence. Dartmouth College.
Ministère des Postes et des Télécommunications. (2016). Strategie, Senegal Numerique 2016–2025. Retrieved from https://www.sec.gouv.sn/sites/default/files/Strat%C3%A9gie%20S%C3%A9n%C3%A9gal%20Num%C3%A9rique%202016–2025.pdf.
Mittelstadt, B.D., Allo, P., Taddeo, M., Wachter, S., Floridi, L., The ethics of algorithms: Mapping the debate. Big Data & Society 3:2 (2016), 1–21.
Najdawi, A. (2020). Assessing AI readiness across organizations: The case of UAE. In 11th international conference on computing, communication and networking technologies (ICCCNT) (pp. 1–5). IEEE.
Nambisan, S., Wright, M., Feldman, M., The digital transformation of innovation and entrepreneurship: Progress, challenges and key themes. Research Policy, 48(8), 2019, 103773.
National Strategy for Artificial Intelligence. (2018). Retrieved from 〈http://niti.gov.in/national-strategy-artificial-intelligence〉.
NetMob Conference. (2015). Book of Abstract. MIT Media Lab, April 2015. Retrieved from https://netmob.org/www15.
Nurmaini, S., The artificial intelligence readiness for pandemic outbreak COVID-19: Case of limitations and challenges in Indonesia. Computer Engineering and Applications Journal 10:1 (2021), 9–19.
O'Neil, C., Weapons of math destruction: How big data increases inequality and threatens democracy. 2016, Crown Publishing Group, New York, NY.
O'Rourke, K.H., Rahman, A.S., Taylor, A.M., Luddites, the industrial revolution, and the demographic transition. Journal of Economic Growth 18:4 (2013), 373–409.
Osterwalder, A., Pigneur, Y., Business model generation: A handbook for visionaries, game changers, and challengers. 2010, John Wiley & Sons.
Parnell, S., Oldfield, S., The Routledge handbook on cities of the global south. 2014, Routledge.
Pasquale, F., The black box society. The secret algorithms that control money and information, 2015, Harvard University Press, Cambridge, MA.
Purdy, M., & Daugherty, P. (2016). Why artificial intelligence is the future of growth. Edited by Accenture. Retrieved from https://www.accenture.com/ve-es/_acnmedia/PDF-33/Accenture-Why-AI-is-the-Future-of-Growth–Country-Spotlights.pdf.
Rai, A., Constantinides, P., Sarker, S., Next generation digital platforms: Toward human-AI hybrids. Mis Quarterly 43:1 (2019), iii–ix.
Sagna, O., De la Domination Politique à la Domination Economique: Une Histoire des Télécommunications au Sénégal. Tic & Société, 2012.
Sarr, E. N., Sall, O., & Diallo, A. (2018). SnVera: A new algorithm for automation of fact-checking in web journalism context. In 2018 fifth international conference on social networks analysis, management and security (SNAMS) (pp. 342–348).
Saunders, M., Lewis, P., Thornhill, A., Research Methods for Business Students, 7th ed, 2016, Pearson, Harlow.
Sayogo, D.S., Yuli, S.B.C., Wiyono, W., Challenges and critical factors of interagency information sharing in Indonesia. Transforming Government: People, Process and Policy 14:5 (2020), 791–806.
Scharff, C., Crompton, H., Traxler, John, (eds.) Mobile device literacy: Status and needs of women in Senegal, 2020, Routledge, UK.
Schinasi, J., Practicing privacy online: Examining data protection regulations through google's global expansion. Columbia Journal of Transnational Law 52:2 (2014), 569–618.
Schurz, G., Patterns of abduction. Synthese, 2008, 10.1007/s11229-007-9223-4.
Te Velde, D. W., Chandarany, O., Hokkheang, H., Monyoudom, Y., Kelsall, T., Lemma, A., … Evans, J. (2020). Fostering an inclusive digital transformation in Cambodia. Supporting Economic Transformation. Retrieved from https://set.odi.org/digital-economy-consultation.
USAID. (2015). Service exports for growth and development: Case studies for Africa. Retrieved from https://pdf.usaid.gov/pdf_docs/PA00MHPQ.pdf.
Verhoef, P.C., Broekhuizen, T., Bart, Y., Bhattacharya, A., Qi Dong, J., Fabian, N., Haenlein, M., Digital transformation: A multidisciplinary reflection and research agenda. Journal of Business Research, 2019, 122.
Visvizi, A., Lytras, M., Rescaling and refocusing smart cities research: From mega cities to smart villages. Journal of Science and Technology Policy Management (JSTPM), 2018.
Vuong, Q.H., Ho, M.T., Vuong, T.T., La, V.P., Ho, M.T., Nghiem, K.C.P., Ho, R., Artificial intelligence vs. natural stupidity: Evaluating AI readiness for the Vietnamese Medical Information System. Journal of Clinical Medicine, 8(2), 2019, 168.
Wautelet, Y., Representing, modeling and engineering a collaborativesupply chain management platform. International Journal of Information Systems and SupplyChain Management (IJISSCM) 5:3 (2012), 1–23.
Wautelet, Y., A model-driven IT governance process based on the strategic impact evaluation of services. Journal of Systems and Software 149 (2019), 462–475.
Wautelet, Y., Kolp, M., Penserini, L., Service-driven iterative software project management with i-tropos. Journal of Universal Computer Science 24:7 (2018), 975–1011.
Wirtz, B.W., Weyerer, J.C., Geyer, C., Artificial intelligence and the public sector—Applications and challenges. International Journal of Public Administration 42:7 (2019), 596–615.
World Economic Forum (WEF). (2016). The networked readiness index 2016. Retrieved from http://www3.weforum.org/docs/GITR2016/WEF_GITR_Chapter1.1_2016.pdf.
Yang, M., & Sovann, D. (2020). Digital “Government-to-Business” services in Cambodia: Overview and challenges. Retrieved from https://www.kas.de/en/web/kambodscha/single-title/-/content/digital-government-to-business-services-in-cambodia-overview-and-challenges.
Yu, E., Giorgini, P., Maiden, N., Mylopoulos, J., Social modeling for requirements engineering. 2011, MIT Press.