Wage Disparities and Gender Gap in STEM Fields – Investigating the Long-term Impact with AI
DOI:
https://doi.org/10.64137/31080030/IJFEMS-V2I3P110Keywords:
AI Bias, STEM Fields, Gender Gap, Artificial Intelligence, Job Opportunities, Gender InequalitiesAbstract
This study explores how artificial intelligence (AI) and other technological advances can align with the gender-based wage gap and their implications for job opportunities over time. Irrespective of changes in technology and social norms, the study highlights that there are significant concerns related to the potential of AI to either increase or mitigate current gender inequalities. Though there are opportunities available in AI for fairness and decision-making, there are significant risks that poor representation in STEM and biased algorithms may worsen the gender gap. This study is based on a critical analysis of the ethical and structural challenges in deploying AI and the overlooking of women in the governance and design of AI programs. There is also an uneven effect of automation on industries led by women. With a systematic review approach, this study has identified various sources of AI bias, their implications, persistent gaps in the promotion and participation of women in STEM fields, and both policy-based and technical ways to arrive at more equitable outcomes as AI becomes increasingly integrated into society.
References
[1] A. Agrawal, J. S. Gans, and A. Goldfarb, “Prediction, Judgment and Complexity: A Theory of Decision Making and Artificial Intelligence,” SSRN Electronic Journal, 2018, doi: 10.2139/ssrn.3103156.
[2] M. Ananny and K. Crawford, “Seeing without knowing: Limitations of the transparency ideal and its application to algorithmic accountability,” New media & Society, vol. 20, no. 3, 2018, doi: 10.1177/1461444816676645.
[3] J. Angwin, J. A. Larson, S. Mattu, and L. Kirchner, “Machine Bias *,” pp. 254–264, Mar. 2022, doi: 10.1201/9781003278290-37.
[4] E. Arrigo, A. Di Vaio, R. Hassan, and R. Palladino, “Followership behavior and corporate social responsibility disclosure: Analysis and implications for sustainability research,” Journal of Cleaner Production, vol. 360, p. 132151, Aug. 2022, doi: 10.1016/j.jclepro.2022.132151.
[5] “Barocas, S., & Selbst, A. D. (2016). Big Data’s Disparate Impact. California Law Review, 104, 671-732. - References - Scientific Research Publishing,” Scirp.org, 2016. https://www.scirp.org/reference/referencespapers?referenceid=3614745 (accessed Aug. 27, 2026).
[6] J. Buolamwini and T. Gebru, “Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification,” proceedings.mlr.press, Jan. 21, 2018. https://proceedings.mlr.press/v81/buolamwini18a.html?mod=article_inline&ref=akusion-ci-shi-dai-bizinesumedeia (accessed Aug. 27, 2026).
[7] Constantinos Challoumis - Κωνσταντίνος Χαλλουμής, “AI AND ECONOMIC INEQUALITY - ADDRESSING THE CHALLENGES AHEAD,” Dec. 31, 2024. https://www.researchgate.net/publication/387577305_AI_AND_ECONOMIC_INEQUALITY_-_ADDRESSING_THE_CHALLENGES_AHEAD (accessed Aug. 27, 2026).
[8] S. Corbett-Davies and S. Goel, “The Measure and Mismeasure of Fairness: A Critical Review of Fair Machine Learning,” arXiv:1808.00023 [cs], Aug. 2018, Accessed: Aug. 27, 2026. [Online]. Available: https://arxiv.org/abs/1808.00023
[9] S. Corbett-Davies, E. Pierson, A. Feller, S. Goel, and A. Huq, “Algorithmic Decision Making and the Cost of Fairness,” Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Aug. 2017, doi: 10.1145/3097983.3098095.
[10] K. Crawford and R. Calo, “There is a blind spot in AI research,” Nature, vol. 538, no. 7625, pp. 311–313, Oct. 2016, doi: 10.1038/538311a.
[11] C. Dwork, M. Hardt, T. Pitassi, O. Reingold, and R. Zemel, “Fairness through awareness,” Proceedings of the 3rd Innovations in Theoretical Computer Science Conference on - ITCS ’12, 2012, doi: 10.1145/2090236.2090255.
[12] E. Ernst, R. Merola, and D. Samaan, “Economics of Artificial Intelligence: Implications for the Future of Work,” IZA Journal of Labor Policy, vol. 9, no. 1, Aug. 2019, doi: 10.2478/izajolp-2019-0004.
[13] A. Ferguson, “Predictive Policing and Reasonable Suspicion,” Emory Law Journal, vol. 62, no. 2, p. 259, Jan. 2012, Accessed: Aug. 27, 2026. [Online]. Available: https://scholarlycommons.law.emory.edu/elj/vol62/iss2/1/
[14] E. Ferrara, “GenAI against humanity: nefarious applications of generative artificial intelligence and large language models,” Journal of Computational Social Science, vol. 7, Feb. 2024, doi: 10.1007/s42001-024-00250-1.
[15] E. Ferrara, “Should ChatGPT be Biased? Challenges and Risks of Bias in Large Language Models,” arXiv:2304.03738 [cs], Apr. 2023, Accessed: Aug. 27, 2026. [Online]. Available: https://arxiv.org/abs/2304.03738
[16] E. Ferrara, “The Butterfly Effect in artificial intelligence systems: Implications for AI bias and fairness,” Machine Learning with Applications, vol. 15, no. 15, p. 100525, Mar. 2024, doi: 10.1016/j.mlwa.2024.100525.
[17] T. Gebru et al., “Datasheets for datasets,” Communications of the ACM, vol. 64, no. 12, pp. 86–92, Dec. 2021, doi: 10.1145/3458723.
[18] A. Goyal and R. Aneja, “Artificial intelligence and income inequality: Do technological changes and worker’s position matter?,” Journal of Public Affairs, vol. 20, no. 4, Sept. 2020, doi: 10.1002/pa.2326.
[19] F. Kamiran and T. Calders, “Data preprocessing techniques for classification without discrimination,” Knowledge and Information Systems, vol. 33, no. 1, pp. 1–33, Dec. 2011, doi: 10.1007/s10115-011-0463-8.
[20] J. Kleinberg, H. Lakkaraju, J. Leskovec, J. Ludwig, and S. Mullainathan, “Human Decisions and Machine Predictions,” The Quarterly Journal of Economics, vol. 133, no. 1, Aug. 2017, doi: 10.1093/qje/qjx032.
[21] W. M. Lim, S. Kumar, and F. Ali, “Advancing Knowledge through Literature reviews: ‘what,’ ‘why,’ and ‘how to Contribute,’” The Service Industries Journal, vol. 42, no. 7–8, pp. 481–513, Mar. 2022, doi: https://doi.org/10.1080/02642069.2022.2047941.
[22] B. D. Mittelstadt, P. Allo, M. Taddeo, S. Wachter, and L. Floridi, “The Ethics of algorithms: Mapping the Debate,” Big Data & Society, vol. 3, no. 2, pp. 1–21, Dec. 2016, doi: 10.1177/2053951716679679.
[23] Z. Obermeyer, B. Powers, C. Vogeli, and S. Mullainathan, “Dissecting Racial Bias in an Algorithm Used to Manage the Health of Populations,” Science, vol. 366, no. 6464, pp. 447–453, Oct. 2019, doi: 10.1126/science.aax2342.
[24] K. K. Pal, K. Piaget, Saadia Zahidi, and S. Baller, “Global Gender Gap Report 2024,” World Economic Forum, June 11, 2024. https://www.weforum.org/publications/global-gender-gap-report-2024/?gad_source=1&gad_campaignid=22228224717&gbraid=0AAAAAoVy5F4x6KcDDQCh4ldE2ZECxmJGX&gclid=Cj0KCQjwnbrUBhDOARIsAKKhPpeO-fLhsfYL4prGqNsYy5MQOpDnWU5BYLWoK-tsqh3PUiIgtd7Y1s0aAm9kEALw_wcB (accessed Aug. 27, 2026).
[25] “Workmonitor | Randstad,” www.randstad.com. https://www.randstad.com/workmonitor/
[26] R. Schwartz, A. Vassilev, K. K. Greene, L. Perine, A. Burt, and P. Hall, “Towards a Standard for Identifying and Managing Bias in Artificial Intelligence,” NIST, Mar. 2022, Accessed: Aug. 27, 2026. [Online]. Available: https://www.nist.gov/publications/towards-standard-identifying-and-managing-bias-artificial-intelligence
[27] A. D. Selbst, D. Boyd, S. A. Friedler, S. Venkatasubramanian, and J. Vertesi, “Fairness and Abstraction in Sociotechnical Systems,” Proceedings of the Conference on Fairness, Accountability, and Transparency - FAT* ’19, pp. 59–68, Jan. 2019, doi: 10.1145/3287560.3287598.
[28] H. Snyder, “Literature review as a research methodology: An overview and guidelines,” Journal of Business Research, vol. 104, no. 1, pp. 333–339, Nov. 2019, doi: https://doi.org/10.1016/j.jbusres.2019.07.039.
[29] L. Sweeney, “Discrimination in online ad delivery,” Communications of the ACM, vol. 56, no. 5, pp. 44–54, May 2013, doi: 10.1145/2447976.2447990.
[30] J. Truby, “Governing Artificial Intelligence to benefit the UN Sustainable Development Goals,” Sustainable Development, vol. 28, no. 4, Feb. 2020, doi: 10.1002/sd.2048.
[31] A. Turing, “Computing Machinery and Intelligence,” Mind, vol. 59, no. 236, pp. 433–460, Oct. 1950, doi: https://doi.org/10.1093/mind/LIX.236.433.
[32] Z. Tzannatos, “Women and Labor Market Changes in the Global Economy: Growth Helps, Inequalities Hurt and Public Policy Matters,” World Development, vol. 27, no. 3, pp. 551–569, Mar. 1999, doi: 10.1016/S0305-750X(98)00156-9.
[33] R. Vinuesa et al., “The role of artificial intelligence in achieving the Sustainable Development Goals,” Nature Communications, vol. 11, no. 1, p. 233, Jan. 2020, doi: 10.1038/s41467-019-14108-y.
[34] S. Wachter, B. Mittelstadt, and C. Russell, “Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR,” SSRN Electronic Journal, vol. 31, no. 2, 2017, doi: 10.2139/ssrn.3063289.
[35] World Economic Forum, Gender parity in the intelligent age, White Paper, pp. 1-20, World Economic Forum, 2025. [Online]. Available: https://reports.weforum.org/docs/WEF_Gender_Parity_in_the_Intelligent_Age_2025.pdf
[36] S. Yan, H. Kao, and E. Ferrara, “Fair Class Balancing: Enhancing Model Fairness without Observing Sensitive Attributes,” Proceedings of the 29th ACM International Conference on Information & Knowledge Management, Oct. 2020, doi: 10.1145/3340531.3411980.
[37] C. Yu, “Gender Inequality in the Age of AI: Predictions, Perspectives, and Policy Recommendations,” Jan. 2024, doi: 10.31219/osf.io/5zrh9.
[38] M. B. Zafar, I. Valera, M. Gomez Rodriguez, and K. P. Gummadi, “Fairness Beyond Disparate Treatment & Disparate Impact,” Proceedings of the 26th International Conference on World Wide Web - WWW ’17, 2017, doi: 10.1145/3038912.3052660.
[39] I. Žliobaitė, “Measuring discrimination in algorithmic decision making,” Data Mining and Knowledge Discovery, vol. 31, no. 4, pp. 1060–1089, Mar. 2017, doi: 10.1007/s10618-017-0506-1.
