Empowering Women in the Digital Age : Leveraging AI for Economic Inclusion and Advancement
DOI:
https://doi.org/10.17010/aijer/2024/v13i4/174047Keywords:
women empowerment
, artificial intelligence (AI), economic inclusion, gender equality.JEL Classification Codes
, G15, I3, O1Paper Submission Date
, July 4, 2024, Paper sent back for Revision, March 6, Paper Acceptance Date, May 15, 2024Abstract
Purpose : Gender equality and inclusive economic growth enable women to have control over resources, employment, and entrepreneurship. This has the potential to encourage involvement and have a good economic impact. In light of the changing environment, this study investigated how artificial intelligence (AI) may support the objective of women’s empowerment.
Research Methodology : The research methodology for this study was secondary research, specifically thematic analysis and observational study. A wide range of sources, including academic journals, books, research reports, and reputable online platforms, were analyzed alongside analysis of AI-based women empowerment initiatives undertaken across various economies.
Findings : The results showed that hiring and employment practices may be made less restrictive by utilizing AI, which would improve the representation of women in traditionally male-dominated fields. Modern technology has also made it simpler to identify and address gender pay disparities, ensuring equitable remuneration. Virtual assistants with AI capabilities have made financial services more accessible to women, enabling them to make well-informed financial decisions and strategic investments. The study highlighted how AI benefits women’s health by enhancing healthcare outcomes with precise diagnosis and practical telemedicine solutions.
Practical Implications : This study has implications for policymakers, advocates for women’s rights, AI developers, and gender equality campaigners. Economic empowerment programs, technological innovation, and policy frameworks that are focused on women can all benefit from these ideas. Economic success can be reconfigured for a future focused on growth by optimizing women’s potential equity.
Originality Value : This study has original insights into the intersection of gender disparities, AI accessibility, and empowerment, informing strategies for inclusive development.
Downloads
Published
How to Cite
Issue
Section
References
Abdulkareem, L. R., & Karan, O. (2022). Using ANN to predict gender-based violence in Iraq: How AI and data mining technologies revolutionized social networks to make a safer world. In 2022 International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT). IEEE. https://doi.org/10.1109/ISMSIT56059.2022.9932831
Ada Lovelace Institute. (2024, November 15). About us. https://www.adalovelaceinstitute.org/about/
AI for Good. (2023, July 6). AI for Good: Global submit. https://aiforgood.itu.int/summit23/
Alavanja, M. C., Samanic, C., Dosemeci, M., Lubin, J., Tarone, R., Lynch, C. F., Knott, C., Thomas, K., Hoppin, J. A., Barker, J., Coble, J., Sandler, D. P., & Blair, A. (2003). Use of agricultural pesticides and prostate cancer risk in the agricultural health study cohort. American Journal of Epidemiology, 157(9), 800–814. https://doi.org/10.1093/aje/kwg040
Bagdy, G., & Juhasz, G. (2013). Biomarkers for personalized treatment in psychiatric diseases. Expert Opinion on Medical Diagnostics, 7(5), 417–422. https://doi.org/10.1517/17530059.2013.821979
Bartlett, F. C. (1932). Remembering: A study in experimental and social psychology. Cambridge University Press.
Basumatary, R., & Das, M. (2018). Gender inequality in education and the reasons of its variation across Assam, India. Arthshastra Indian Journal of Economics & Research, 7(2), 21–37. https://doi.org/10.17010/aijer/2018/v7i2/125136
Bhattacharya, J., & Banerjee, S. (2012). Women empowerment as multidimensional capability enhancement: An application of structural equation modeling. Poverty & Public Policy, 4(3), 79–98. https://doi.org/10.1002/pop4.7
Blacker, R., Kurtz, A., & Goodwin, A. (2017). An in-depth observational study of an acute psychiatric ward: Combining the psychodynamic observational method with thematic analysis to develop understanding of ward culture. Psychoanalytic Psychotherapy, 31(1), 4–20. https://doi.org/10.1080/02668734.2016.1275037
Bonaccolto-Töpfer, M., & Briel, S. (2022). The gender pay gap revisited: Does machine learning offer new insights? Labour Economics, 78, Article ID 102223. https://doi.org/10.1016/j.labeco.2022.102223
Bordoloi, R. (2015). Gender inequalities: A reflection on the Indian education system. Arthshastra Indian Journal of Economics & Research, 4(6), 34–42. https://doi.org/10.17010/aijer/2015/v4i6/84919
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa
Capelli, G., Glavas, D., Ferrari, L., Verdi, D., & Spolverato, G. (2023). Women surgeons fighting for work-life balance: How technology might help close the gender gap. Artificial Intelligence Surgery, 3, 80–89. https://doi.org/10.20517/ais.2022.40
Chaudhary, A. R., Chani, M. I., & Pervaiz, Z. (2012). An analysis of different approaches to women empowerment: A case study of Pakistan. World Applied Sciences Journal, 16(7), 971–980.
Clement, F. (2024, November 15). Women in AI Canada. https://www.womeninai.co/canada
Council of Economic Advisers. (2019, February 7). Relationship between female labor force participation rates and GDP. The White House. https://trumpwhitehouse.archives.gov/articles/relationship-female-labor-force-participation-rates-gdp/
Deshpande, P. (2023, March 8). Two out of three women dependent on men for financial decisions: Survey. CNBCTV18. https://www.cnbctv18.com/personal-finance/international-womens-day-indialends-working-women-financial-independence-survey-findings-16114581.htm
European Institute for Gender Equality (EIGE). (2021, December 9). Artificial intelligence, platform work and gender equality. EIGE.
FINCA. (2021, November 9). Financial literacy strengthens women's inclusion. https://finca.org/blogs/financial-literacy-strengthens-womens-inclusion
Goel, S., & Chakravarty, S. L. (2023). Working as a domestic maid: Survival strategy for poor women. Arthshastra Indian Journal of Economics & Research, 12(3), 8–20. https://doi.org/10.17010/aijer/2023/v12i3/173258
Hamdan, A., Hassanien, A. E., Khamis, R., Alareeni, B., Razzaque, A., & Awwad, B. (2021). Applications of artificial intelligence in business, education and healthcare (1st ed.). Springer. https://doi.org/10.1007/978-3-030-72080-3
Heard, E., Fitzgerald, L., Whittaker, M., Va'ai, S., & Mutch, A. (2020). Exploring intimate partner violence in Polynesia: A scoping review. Trauma, Violence, & Abuse, 21(4), 769–778. https://doi.org/10.1177/1524838018795504
Heneghan, L. (2023). Five ways tech can close the gender gap. KPMG.
Houssami, N., Kirkpatrick-Jones, G., Noguchi, N., & Lee, C. I. (2019). Artificial intelligence (AI) for the early detection of breast cancer: A scoping review to assess AI's potential in breast screening practice. Expert Review of Medical Devices, 16(5), 351–362. https://doi.org/10.1080/17434440.2019.1610387
India Lends. (2023). Workingstree survey. Delhi: India Lends.
Jabbar, S. A., & Zaza, H. I. (2016). Evaluating a vocational training programme for women refugees at the Zaatari camp in Jordan: Women empowerment: A journey and not an output. International Journal of Adolescence and Youth, 21(3), 304–319. https://doi.org/10.1080/02673843.2015.1077716
Jheng, Y.-C., Kao, C.-L., Yarmishyn, A. A., Chou, Y.-B., Hsu, C.-C., Lin, T.-C., Hu, H.-K., Ho, T.-K., Chen, P.-Y., Kao, Z.-K., Chen, S.-J., & Hwang, D.-K. (2020). The era of artificial intelligence-based individualized telemedicine is coming. Journal of the Chinese Medical Association, 83(11), 981–983. https://doi.org/10.1097/jcma.0000000000000374
Jora, R. B., Sodhi, K. K., Mittal, P., & Saxena, P. (2022). Role of artificial intelligence (AI) in meeting diversity, equality, and inclusion (DEI) goals. In 8th International Conference on Advanced Computing and Communication Systems (Vol. 1, pp. 1687–1690). IEEE. https://doi.org/10.1109/ICACCS54159.2022.9785266
Kelan, E. K. (2024). Algorithmic inclusion: Shaping the predictive algorithms of artificial intelligence in hiring. Human Resource Management Journal, 34(3), 694–707. https://doi.org/10.1111/1748-8583.12511
Khan, S. (2023, March 8). How biases in AI technology has made gender inequality more visible. The Palladium Group. https://thepalladiumgroup.com/news/How-Biases-in-AI-Technology-Has-Made-Gender-Inequality-More-Visible
Khera, P., Ogawa, S., Sahay, R., & Vasishth, M. (2022, December). The digital gender gap. International Monetary Fund. https://www.imf.org/en/Publications/fandd/issues/2022/12/the-digital-gender-gap-khera-ogawa-sahay-vasishth
Khosla, R., & Tara, D. (2019). Artificial intelligence and robotics - Transforming the industrial economies. Arthshastra Indian Journal of Economics & Research, 8(5), 16–21. https://doi.org/10.17010/aijer/2019/v8i5/149679
Kuroda, R., Lopez, M., Sasaki, J., & Settlecase, M. (2020). The digital gender gap. EY-GSMA.
Madakam, S., & Ramaswamy, R. (2014). Smart homes (conceptual views). In 2nd International Symposium on Computational and Business Intelligence (pp. 63–66). IEEE. https://doi.org/10.1109/ISCBI.2014.21
Marwala, T. (2024, March 7). Now is our chance to govern AI for women's empowerment. The Japan Times. https://www.japantimes.co.jp/commentary/2024/03/07/world/ai-developement-women-empowerment/
Nuseir, M. T., Al Kurdi, B. H., Alshurideh, M. T., & Alzoubi, H. M. (2021). Gender discrimination at workplace: Do artificial intelligence (AI) and machine learning (ML) have opinions about it. In A. E. Hassanien, A. Haqiq, P. J. Tonellato, L. Bellatreche, S. Goundar, A. T. Azar, E. Sabir, & D. Bouzidi (eds.), Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2021). Advances in Intelligent Systems and Computing (Vol. 1377, pp. 301–316). Springer. https://doi.org/10.1007/978-3-030-76346-6_28
Paigude, S. D., & Shikalgar, S. R. (2023). Deep learning model for work-life balance prediction for working women in IT Industry. In Proceedings of the 4th International Conference on Information Management & Machine Intelligence (Article no. 9, pp. 1–8). Association for Computing Machinery. https://doi.org/10.1145/3590837.3590846
Parvathy, P., & Kavitha A. C. (2023). Influence of individual and household characteristics on unpaid work of women – An exposition of the case of Malayali women. Arthshastra Indian Journal of Economics & Research, 12(4), 52–64. https://doi.org/10.17010/aijer/2023/v12i4/173306
Patanjali, S., & Subramaniam, D. (2019). India, the fourth industrial revolution and government policy. Arthshastra Indian Journal of Economics & Research, 8(2), 32–44. https://doi.org/10.17010/aijer/2019/v8i2/145224
Paul G. Allen School of Computer Science and Engineering. (2024, November 15). Google Anita Borg scholarship. University of Washington. https://www.cs.washington.edu/students/grad/awardrecipients/borg
Picatoste, X., Mesquita, A., & González-Laxe, F. (2023). Gender wage gap, quality of earnings and gender digital divide in the European context. Empirica, 50, 301–321. https://doi.org/10.1007/s10663-022-09555-8
Ramakrishnan, R., Rao, S., & He, J.-R. (2021). Perinatal health predictors using artificial intelligence: A review. Women's Health, 17. https://doi.org/10.1177/17455065211046132
Ramos, G. (2022, August 22). Why we must act now to close the gender gap in AI. World Economic Forum. https://www.weforum.org/stories/2022/08/why-we-must-act-now-to-close-the-gender-gap-in-ai/
Redondo, R. P., Vilas, A. F., Merino, M. R., Valladares-Rodríguez, S. M., Guijarro, S. T., & Hafez, M. M. (2023). Anti-sexism alert system: Identification of sexist comments on social media using AI Techniques. Applied Sciences, 13(7), 4341. https://doi.org/10.3390/app13074341
Ricaurte, P. (2022). Ethics for the majority world: AI and the question of violence at scale. Media, Culture & Society, 44(4), 726–745. https://doi.org/10.1177/01634437221099612
Schwendimann, R., Blatter, C., Lüthy, M., Mohr, G., Girard, T., Batzer, S., Davis, E., & Hoffmann, H. (2019). Adherence to the WHO surgical safety checklist: An observational study in a Swiss academic center. Patient Safety in Surgery, 13(1), Article no. 14. https://doi.org/10.1186/s13037-019-0194-4
Sicat, M., Xu, A., Mehetaj, E., Ferrantino, M., & Chemutai, V. (2020). Leveraging ICT technologies in closing the gender gap. World Bank Group. http://documents.worldbank.org/curated/en/891391578289050252/Leveraging-ICT-Technologies-in-Closing-the-Gender-Gap
Silva, L. (2024, January 11). How artificial general intelligence will drive an inclusive financial sector in Latin America. World Economic Forum. https://www.weforum.org/stories/2024/01/ai-is-driving-the-evolution-of-a-more-inclusive-financial-sector-in-latin-america-here-is-how/
The Alan Turing Institute. (2024, November 15). Women in data science and AI. https://www.turing.ac.uk/research/research-programmes/public-policy/public-policy-themes/women-data-science-and-ai
UNESCO. (2020). Artificial intelligence and gender equality: Key findings of UNESCO's global dialogue. https://unesdoc.unesco.org/ark:/48223/pf0000374174
von Hogersthal, G. B. (2023, October 10). Artificial intelligence and alternative data in credit scoring and credit risk surveillance. S&P Global. https://www.spglobal.com/en/research-insights/special-reports/artificial-intelligence-and-alternative-data-in-credit-scoring-and-credit-risk-surveillance
Wang, F. (2018). The roles of preventive and curative health care in economic development. PLoS One, 13(11), e0206808. https://doi.org/10.1371/journal.pone.0206808
Women Entrepreneurship Platform. (WEP). (2024, November 15). WEP's award-to-reward program. https://wep.gov.in/
Women In Technology International (WITI). (2024, November 15). About WITI. https://witi.com/about/
World Bank Group. (2022). Nearly 2.4 billion women globally don't have same economic rights as men. The World Bank. https://www.worldbank.org/en/news/press-release/2022/03/01/nearly-2-4-billion-women-globally-don-t-have-same-economic-rights-as-men
Zacharia, Z. C., Hovardas, T., Xenofontos, N., Pavlou, I., & Irakleou, M. (2020). Education and employment of women in science, technology and the digital economy, including AI and its influence on gender equality. Think Tank European Parliament. https://www.europarl.europa.eu/thinktank/en/document/IPOL_STU(2020)651042