Leveraging Artificial Intelligence in the Field of Social Policy against Social Inequalities: The Current Landscape


Published: Sep 12, 2024
Keywords:
AI Economic Empowerment Education Employment Healthcare Social Inequalities Social Policy
Georgios Tsertekidis
Periklis Polyzoidis
Abstract

Artificial Intelligence (AI) has become an integral part of daily human activity in both societal and political terms. Thus, it has also rapidly grown into a tool of ever-growing importance for addressing social inequalities. The increasing adoption of AI in various economical and societal aspects of daily life has significant implications for social welfare and policy development. This commentary attempts to bring the issue of the deployment of AI in social policy into the fore, examining its potential in countering and mitigating inequalities and disparities in various areas of life such as healthcare, education, and economic empowerment. There seems to be a scientific consensus that AI can be effectively utilized to mitigate social inequalities.

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References
Acemoglu, D., & Restrepo, P. (2018). Artificial Intelligence, Automation, and Work. In The Economics of Artificial Intelligence: An Agenda (pp. 197-236). University of Chicago Press.
Baker, R., & Siemens, G. (2014). Educational data mining and learning analytics. In Sawyer, R. K. (Ed.), The Cambridge Handbook of the Learning Sciences. 253-272. Cambridge University Press.
Bohnenberger, K. (2023). Peaks and gaps in eco-social policy and sustainable welfare: A systematic literature map of the research landscape. European Journal of Social Security, 25, 328 - 346.
Brynjolfsson, E., & McAfee, A. (2017). The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. W. W. Norton & Company.
Dai, H. (2013). Social Inequality in a Bonded Community: Community Ties and Villager Resistance in a Chinese Township. Social Service Review, 87, 269 - 291.
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115–118. https://doi.org/10.1038/nature21056
Gogas, P., Papadimitriou, T., & Sofianos, E. (2022). Forecasting unemployment in the euro area with machine learning. Journal of Forecasting, 41(3), 551–566. https://doi.org/10.1002/for.2824
Henman, P. W. F. (2022). Digital Social Policy: Past, Present, Future. Journal of Social Policy, 51(3), 535–550. doi:10.1017/S0047279422000162
Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. Center for Curriculum Redesign.
Johnson, L., Adams Becker, S., Estrada, V., & Freeman, A. (2016). NMC Horizon Report: 2016 Higher Education Edition. The New Media Consortium.
Kulik, J. A., & Fletcher, J. D. (2016). Effectiveness of Intelligent Tutoring Systems: A Meta-Analytic Review. Review of Educational Research, 86(1), 42-78.
Lohmann, G., Lobo, H.A., Trigo, L.G., Valduga, V., Castro, R., Coelho, M.D., Cyrillo, M.W., Dalonso, Y.D., Gimenes-Minasse, M.H., Gosling, M.D., Lanzarini, R., Leal, S.R., Marques, O., Mayer, V.F., Moreira, J.C., Moraes, L.A., Netto, A.P., Perinotto, A.R., Neto, A.Q., Trentin, F., & Raimundo, S. (2021). Tourism in Brazil: from politics, social inequality, corruption and violence towards the 2030 Brazilian tourism agenda. Tourism Review.
Margalit, Y., & Raviv, S. (2023). The Politics of Using AI in Public Policy: Experimental Evidence. SSRN Electronic Journal. http://dx.doi.org/10.2139/ssrn.4573250
Mehrabi, N., Morstatter, F., Saxena, N.A., Lerman, K., & Galstyan, A.G. (2019). A Survey on Bias and Fairness in Machine Learning. ACM Computing Surveys (CSUR), 54, 1 - 35.
Michailidis, P., Dimitriadou, A., Papadimitriou, T., Gogas, P. (2022). Forecasting Hospital Readmissions with Machine Learning. Healthcare. 10(6):981. https://doi.org/10.3390/healthcare10060981
Mustafa, A. (2023). Book Review: Social Policy in Changing European Societies. Research agendas for the 21st Century by K. Nelson, et al. European Journal of Social Security, 25, 98 - 100.
Obermeyer, Z., & Emanuel, E. J. (2016). Predicting the Future - Big Data, Machine Learning, and Clinical Medicine. The New England journal of medicine, 375(13), 1216–1219. https://doi.org/10.1056/NEJMp1606181
Pane, J. F., Griffin, B. A., McCaffrey, D. F., & Karam, R. (2014). Effectiveness of Cognitive Tutor Algebra I at Scale. Educational Evaluation and Policy Analysis, 36(2), 127-144.
Peña-Acuña, B. (2023). Trending Topics about Performance in Second Language Learning. East European Journal of Psycholinguistics, 10(1), 177-199.
Rajkomar, A., Dean, J., & Kohane, I. (2019). Machine Learning in Medicine. The New England journal of medicine, 380(14), 1347–1358. https://doi.org/10.1056/NEJMra1814259
Ruiz Estrada, M., Park, D., & Staniewski, M. (2023). Artificial Intelligence (AI) Can Change the Way of Doing Policy Modelling. Journal of Policy Modeling. 45. 1099-1116. https://doi.org/10.1016/j.jpolmod.2023.11.005
Schiff, D.S. (2021). Education for AI, not AI for Education: The Role of Education and Ethics in National AI Policy Strategies. International Journal of Artificial Intelligence in Education, 32, 527 - 563.
VanLehn, K. (2011). The Relative Effectiveness of Human Tutoring, Intelligent Tutoring Systems, and Other Tutoring Systems. Educational Psychologist, 46(4), 197-221.