Artificial Intelligence and Inequality: Examining the Social Divides Created by Technological Advancements
DOI:
https://doi.org/10.69968/ijisem.2024v3si2228-236Keywords:
Artificial Intelligence, Social Inequality, Digital Divide, Marginalized CommunitiesAbstract
The integration of Artificial Intelligence (AI) holds the potential to profoundly transform society in various ways. From a sociological perspective, these advancements may exacerbate pre-existing social inequalities. This research critically explores how AI technologies contribute to the preservation and even expansion of societal disparities, particularly those related to gender, race, and class. It investigates the impact of AI-driven automation on labor markets, underscoring the disproportionate effects on low-income and marginalized groups, which may lead to job loss and increased economic inequality. The analysis also addresses the issue of the "digital divide," highlighting how unequal access to AI technologies and educational resources can reinforce social stratification. Furthermore, the paper examines algorithmic bias, which poses a risk of perpetuating systemic discrimination against disadvantaged communities. The study underscores the importance of developing AI policies grounded in sociological principles that prioritize social justice and equity to mitigate the risk of AI-induced inequality.
References
[1] Binns, R. (2018). Fairness in machine learning: Lessons from political philosophy. Proceedings of the 2018 Conference on Fairness, Accountability, and Transparency, 149-158. https://doi.org/10.1145/3287560.3287598
[2] Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. W. W. Norton & Company.
[3] Chouldechova, A., & Roth, A. (2018). A snapshot of algorithmic fairness. Communications of the ACM, 61(2), 56-62. https://doi.org/10.1145/3158651
[4] Eubanks, V. (2018). Automating inequality: How high-tech tools profile, police, and punish the poor. St. Martin's Press.
[5] European Commission. (2020). White paper on artificial intelligence: A European approach to excellence and trust. https://doi.org/10.2759/777616
[6] Farahani, M. S., & Ghasemi, G. (2024). Artificial intelligence and inequality: Challenges and opportunities. Qeios. https://doi.org/10.32388/7HWUZ2
[7] Hassani, H., Huang, Z., & Silva, E. (2020). AI and the future of work: An analysis of job displacement and creation. Journal of Business Research, 116, 260-269. https://doi.org/10.1016/j.jbusres.2019.11.026
[8] How artificial intelligence could widen the gap between rich and poor nations. (2020, December 2). IMF. https://www.imf.org/en/Blogs/Articles/2020/12/02/blog-how-artificial-intelligence-could-widen-the-gap-between-rich-and-poor-nations
[9] Irani, L. (2015). Chasing innovation: Making entrepreneurial citizenship.Social Studies of Science, 45(6), 814-828.https://doi.org/10.1177/0306312715587121
[10] Lee, K. F. (2018). AI superpowers: China, Silicon Valley, and the new world order. Houghton Mifflin Harcourt.
[11] Levy, K. (2015). The ethical implications of algorithms and automated decision-making. Journal of Business Ethics, 128(4), 745-758.https://doi.org/10.1007/s10551-014-2314-8
[12] Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447-453. https://doi.org/10.1126/science.aax2342
[13] O'Neil, C. (2016). Weapons of math destruction: How big data increases inequality and threatens democracy. Crown Publishing Group.
[14] Pasquale, F. (2015). The black box society: The secret algorithms that control money and information. Harvard University Press.https://doi.org/10.4159/harvard.9780674736061
[15] Ponce, J., & Torres, E. (2020). Algorithmic bias: A systematic literature review. Artificial Intelligence Review, 53, 1-32. https://doi.org/10.1007/s10462-020-09763-x
[16] Raj, A., & Gollakota, S. (2019). AI and gender inequality: The case of employment in India. Journal of Business Research, 114, 343-351. https://doi.org/10.1016/j.jbusres.2019.08.004
[17] Reuben, E., & Smeesters, K. (2020). Gender bias in AI: A systematic review. Journal of Gender Studies, 29(4), 425-437.https://doi.org/10.1080/09589236.2020.1713308
[18] Schmidt, E., & Cohen, J. (2013). The new digital age: Reshaping the future of people, nations, and business. Knopf.
[19] Sweeney, L. (2013). Discrimination in online ad delivery. Communications of the ACM, 56(5), 44-54. https://doi.org/10.1145/2447976.2447990
[20] Van Dijck, J. (2014). Datafication, dataism and digital life.Big Data & Society, 1(1), 1-10. https://doi.org/10.1177/2053951714528481
[21] Zajko, M. (2022). Artificial intelligence, algorithms, and social inequality: Sociological contributions to contemporary debates. Sociology Compass, 16(3). https://doi.org/10.1111/soc4.12962
[22] Zuboff, S. (2019). The age of surveillance capitalism: The fight for a human future at the new frontier of power. PublicAffairs.
[23] Zwitter, A. (2014). Big data ethics. Big Data & Society, 1(2), 1-6. https://doi.org/10.1177/2053951714559253
[24] Binns, R. (2018). Fairness in machine learning: Lessons from political philosophy. Proceedings of the 2018 Conference on Fairness, Accountability, and Transparency.
[25] Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. W. W. Norton & Company.
[26] Eubanks, V. (2018). Automating inequality: How high-tech tools profile, police, and punish the poor. St. Martin's Press.
[27] Lee, K. F. (2018). AI superpowers: China, Silicon Valley, and the new world order. Houghton Mifflin Harcourt.
[28] O'Neil, C. (2016). Weapons of math destruction: How big data increases inequality and threatens democracy. Crown Publishing Group.
[29] Pasquale, F. (2015). The black box society: The secret algorithms that control money and information. Harvard University Press. https://doi.org/10.4159/harvard.9780674736061
[30] Zuboff, S. (2019). The age of surveillance capitalism: The fight for a human future at the new frontier of power. PublicAffairs.
[31] Raji, I. D., & Buolamwini, J. (2019). Actionable auditing: Investigating the impact of publicly naming biased performance results of commercial AI products. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society (pp. 1-16). https://doi.org/10.1145/3306618.3314244
[32] European Commission. (2020). White paper on artificial intelligence: A European approach to excellence and trust. https://doi.org/10.2759/777616
[33] World Economic Forum. (2020). The future of jobs report 2020. https://www.weforum.org/reports/the-future-of-jobs-report-2020
[34] Pew Research Center. (2017). The future of jobs and jobs training. https://www.pewresearch.org/research/future-of-jobs-and-jobs-training/
[35] United Nations Educational, Scientific and Cultural Organization (UNESCO). (2021). AI and education: Guidance for policy-makers. https://unesdoc.unesco.org/ark:/48223/pf0000379687
[36] McKinsey Global Institute. (2017). A future that works: Automation, employment, and productivity. https://www.mckinsey.com/featured-insights/future-of-work/a-future-that-works-automation-employment-and-productivity
[37] OECD. (2021). Artificial intelligence in society. https://doi.org/10.1787/aeed1e95-en
[38] National Academies of Sciences, Engineering, and Medicine. (2019). AI and machine learning for public health: Opportunities and challenges. https://doi.org/10.17226/25530
[39] World Bank. (2019). World development report 2019: The changing nature of work. https://www.worldbank.org/en/publication/wdr2019
[40] AI Now Institute. (2018). Algorithmic impact assessments: A practical framework for public agency accountability. https://ainowinstitute.org/aiareport2018.pdf
[41] Brookings Institution. (2020). AI, the future of work, and the economy. https://www.brookings.edu/research/ai-the-future-of-work-and-the-economy/
[42] MIT Task Force on the Work of the Future. (2020). The work of the future: Shaping technology and institutions. https://workofthefuture.mit.edu/research/
[43] Capgemini Research Institute. (2019). AI and the future of work: The case for a diverse workforce. https://www.capgemini.com/wp-content/uploads/2019/06/AI-and-the-Future-of-Work-Report.pdf
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Nidhi Dinker

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Re-users must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. This license allows for redistribution, commercial and non-commercial, as long as the original work is properly credited.