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.

1. 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
2. Ada Lovelace Institute. (2024, November 15). About us. https://www.adalovelaceinstitute.org/about/
3. AI for Good. (2023, July 6). AI for Good: Global submit. https://aiforgood.itu.int/summit23/
4. 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
5. 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
6. Bartlett, F. C. (1932). Remembering: A study in experimental and social psychology. Cambridge University Press.
7. 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
8. 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
9. 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
10. 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
11. 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
12. Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa
13. 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
14. 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.
15. Clement, F. (2024, November 15). Women in AI Canada. https://www.womeninai.co/canada
16. 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/
17. 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
18. European Institute for Gender Equality (EIGE). (2021, December 9). Artificial intelligence, platform work and gender equality. EIGE.
19. FINCA. (2021, November 9). Financial literacy strengthens women's inclusion. https://finca.org/blogs/financial-literacy-strengthens-womens-inclusion
20. 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
21. 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
22. 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
23. Heneghan, L. (2023). Five ways tech can close the gender gap. KPMG.
24. 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
25. India Lends. (2023). Workingstree survey. Delhi: India Lends.
26. 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
27. 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
28. 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
29. 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
30. 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
31. 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
32. 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
33. Kuroda, R., Lopez, M., Sasaki, J., & Settlecase, M. (2020). The digital gender gap. EY-GSMA.
34. 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
35. 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/
36. 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
37. 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
38. 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
39. 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
40. 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
41. 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
42. 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
43. 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/
44. 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
45. 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
46. 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
47. 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
48. 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/
49. 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
50. UNESCO. (2020). Artificial intelligence and gender equality: Key findings of UNESCO's global dialogue. https://unesdoc.unesco.org/ark:/48223/pf0000374174
51. 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
52. 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
53. Women Entrepreneurship Platform. (WEP). (2024, November 15). WEP's award-to-reward program. https://wep.gov.in/
54. Women In Technology International (WITI). (2024, November 15). About WITI. https://witi.com/about/
55. 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
56. 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