Women and Careers in IT and AI in Quebec: A Review of the Literature
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Abstract
In 1843, Ada Lovelace1 wrote the first computer algorithm in history. Yet the field she helped establish remains paradoxically, still largely male-dominated. Today, in the era of artificial intelligence (AI), this underrepresentation of women contributes to perpetuating algorithmic biases that reproduce and amplify existing inequalities. This literature review examines women’s underrepresentation in information technology (IT) and AI in Quebec. The findings highlight several key factors that help explain this disparity and outline a series of strategic intervention pathways. These levers are essential to building a more inclusive technological ecosystem in Quebec.
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References
Aldasoro, I., Armantier, O., Doerr, S., Gambacorta, L., et Oliviero, T. (2024). The gen AI gender gap (BIS Working Paper No. 1197). Bank for International Settlements.
https://www.bis.org/publ/work1197.pdf
Arksey, H., et O’Malley, L. (2005). Scoping studies: Towards a methodological framework. International Journal of Social Research Methodology, 8(1), 19–32.
https://doi.org/10.1080/1364557032000119616
Braun, V., et Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa
Cerit, J., Dindarian, A., et Cilo-van Norel, N. (2020). Women, AI, and the Power of Supporting Communities: A Digital Gender-Support Partnership. Patterns, 1(1), 100009. https://doi.org/10.1016/j.patter.2020.100009
CIFAR. (2017). Pan-Canadian Artificial Intelligence Strategy. https://cifar.ca/ai/strategy dig. watch
Collett, C., Neff, G., et Gomes, L. G. (2022). Les effets de l’IA sur la vie professionnelle des femmes. UNESCO, OECD, IDB. https://unesdoc.unesco.org/ark:/48223/pf0000380925
Comité d’orientation de la grappe québécoise en intelligence artificielle. (2018). Stratégie pour l’essor de l’écosystème québécois en intelligence artificielle [Rapport, 93 p.]. Gouvernement du Québec. https://api.forum-ia.devbeet.com/app/uploads/2020/09/strategie-ia-vdef-19-juin-2018-v8.pdf
Conseil de l’innovation du Québec. (2024). Prêt pour l’IA : Répondre au défi du développement et du déploiement responsables de l’IA au Québec. Gouvernement du Québec. https://conseilinnovation.quebec/wp-content/uploads/2024/02/Rapport_IA_CIQ-1.pdf
Crenshaw, K. (1989). Demarginalizing the Intersection of Race and Sex: A Black Feminist Critique of Antidiscrimination Doctrine. University of Chicago Legal Forum, 1989 (1), 139–167. https://chicagounbound.uchicago.edu/uclf/vol1989/iss1/8
Crenshaw, K. (1991). Mapping the margins: Intersectionality, identity politics, and violence against women of color. Stanford Law Review, 43(6), 1241–1299.
Cukier, W., Ike, D., Broughton, S., Noshiravani, R., Perruzza, S. et Weissling, L. (2023). More than just numbers revisited: Progress on women in engineering since 1992. Diversity Institute, Toronto Metropolitan University et Ontario Society of Professional Engineers. https://www.torontomu.ca/diversity/reports/more_than_just_numbers/
Daraz, L., Chang, B. S., et Bouseh, S. (2022). Inferior: The Challenges of Gender Parity in the Artificial Intelligence Ecosystem—A Case for Canada. Frontiers in Artificial Intelligence, 5. https://doi.org/10.3389/frai.2022.931182
Díaz-Santos, S., Cotino-Arbelo, A. E., Molina-Gil, J., et González-González, C. S. (2024). STEMming the Gap: Empowering Women’s Initiatives in Computer Science. IEEE Global Engineering Education Conference (EDUCON) (pp. 1–9). IEEE. https://doi.org/10.1109/educon60312.2024.10578900
Ding, Y., Liu, J., Lyu, Z., Zhang, K., Schölkopf, B., Jin, Z., et Mihalcea, R. (2023). Voices of Her: Analyzing Gender Differences in the AI Publication World (arXiv:2305.14597). arXiv. https://doi.org/10.48550/arXiv.2305.14597
Drury, M. (2016). Still Alone at the Table? Women Working in Technology Organizations. Dans Connerley, M. L. et Wu, J. (Éds.) Handbook on Well-Being of Working Women. International Handbooks of Quality-of-Life. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-9897-6_18
González, A. S. et Rampino, L. (2024). A design perspective on how to tackle gender biases when developing AI-driven systems. AI and Ethics. https://doi.org/10.1007/s43681-023-00386-2
González-Gallego, S., Hernández-Pérez, M., Alonso-Sánchez, J. A., Hernández-Castellano, P. M., et Quevedo-Gutiérrez, E. G. (2025). A critical examination of the underlying causes of the gender gap in STEM and the influence of computational thinking projects applied in secondary school on STEM Higher Education. Frontiers in Education, 10, Article 1537040. https://doi.org/10.3389/feduc.2025.1537040
Graham, H., Fuertes, V., Egdell, V., et Raeside, R. (2016). Women in ICT and Digital Technologies: An investigation of the barriers to women entering, staying, and progressing in the sector, and actions to ameliorate this—Executive Summary. Northumbria University. http://nrl.northumbria.ac.uk/37153/
Grant, M. J., et Booth, A. (2009). A typology of reviews: An analysis of 14 review types and associated methodologies. Health Information et Libraries Journal, 26(2), 91–108. https://doi.org/10.1111/j.1471-1842.2009.00848.x
Guillaud, H. (2024, 11 juin). Recrutements automatisés : défaillances spécifiques, défaillances systémiques. Dans les Algorithmes. https://danslesalgorithmes.net/2024/06/11/defaillances-specifiques-defaillances-systemiques/
Hajibabaei, A., Schiffauerova, A., et Ebadi, A. (2022). Gender-specific patterns in the artificial intelligence scientific ecosystem. Journal of Informetrics, 16(2), 101275. https://doi.org/10.1016/j.joi.2022.101275
Harvey, V., et Tremblay, D.-G. (2020). Women in the IT sector: Queen Bee and gender judo strategies. Employee Responsibilities and Rights Journal, 32(4), 197–214. https://doi.org/10.1007/s10672-020-09353-z
Harvey, V. et Tremblay, D.-G. (2024). Immigrant Women in IT: Discrimination in Work, and Solutions for Their Employment. Employee Responsibilities and Rights Journal. https://doi.org/10.1007/s10672-024-09501-9
Hupfer, S., Matheson, B., Crossan, G., et Bucaille, A. (2024, Novembre 19). Women and generative AI: The adoption gap is closing fast, but a trust gap persists. Deloitte Insights. https://www.deloitte.com/us/en/insights/industry/technology/technology-media-and-telecom-predictions/2025/women-and-generative-ai.html
Ignatiuc, A., et Stroiu, A. (2024). Dezvoltarea carierei fetelor și femeilor în industria IT: Oportunități și perspective. Smart Society, Technology and Communication, 2, 30–42. https://doi.org/10.53486/sstc.v2.30
Johnson, V., Kiser, A. I. T., et Kappelman, L. A. (2020). Women in IT Management: Significant Gains and Continuing Challenges. Journal of Computer Information Systems, 60(3), 244–255. https://doi.org/10.1080/08874417.2018.1463576
Kirton, G., et Robertson, M. (2018). Sustaining and advancing IT careers: Women’s experiences in a UK-based IT company. Journal of Strategic Information Systems, 27(2), 157–169. https://doi.org/10.1016/j.jsis.2018.01.001
Kijima, R. (2023). Key Takeaways from STEM Working Group Meeting—US-Japan Women Leaders Dialogue. Japan Center for International Exchange. https://jcie.org/wp-content/uploads/2023/10/STEM-Working-Group-Summary.pdf
Kumari, A., et Tiwari, M. (2025). Gender equality and information technology: A bibliometric analysis and emerging future research direction. International Journal of Sociology and Social Policy, 45(7–8), 1–27. https://doi.org/10.1108/IJSSP-02-2025-0138
Liu, X., et Murphy, D. (2022). Applying a Trustworthy AI Framework to Mitigate Bias and Increase Workforce Gender Diversity. IEEE International Symposium on Technology and Society (ISTAS), (pp. 21–28). https://doi.org/10.1109/ISTAS55053.2022.10227119
López Belloso, M. (2022). Women’s Rights Under AI Regulation: Fighting AI Gender Bias Through a Feminist and Intersectional Approach. Dans Custers, B. et Fosch-Villaronga, E. (Éds.) Law and Artificial Intelligence. Information Technology and Law Series, vol 35. T.M.C. Asser Press. https://doi.org/10.1007/978-94-6265-523-2_5
Montingelli, I. M., Malucelli, A., Reinehr, S., et Silla, C. N. (2023). Bringing more girls to STEM: Initiatives within the 2023 IEEE/Frontiers in Education Conference (FIE). Dans Proceedings of the 2023 IEEE Frontiers in Education Conference (FIE) (pp. 1495–1499). IEEE. https://doi.org/10.1109/FIE58773.2023.10343411
Moreira, L., Soares, L., et Martinhago, A. (2024). Inspiring Women in Technology: Educational Pathways and Impact. In Anais do XVIII Women in Information Technology, (pp. 325–330). Porto Alegre: SBC. https://doi.org/10.5753/wit.2024.1910
Munn, Z., Peters, M. D. J., Stern, C., Tufanaru, C., McArthur, A., et Aromataris, E. (2018). Systematic review or scoping review? Guidance for authors when choosing between a systematic or scoping review approach. BMC Medical Research Methodology, 18(1), 143. https://doi.org/10.1186/s12874-018-0611-x
Otis, N. G., Clarke, R. P., Delecourt, S., Cranney, K., et Koning, R. (2024). Global evidence on gender gaps and generative AI. Harvard Business School Working Paper. https://www.hbs.edu/faculty/Pages/item.aspx?num=66548
Pennetier, M. (2025, 7 février). Derrière la révolution de l’IA, la place des femmes en question. Le Devoir. https://www.ledevoir.com/economie/840148/derriere-revolution-ia-quelle-est-place-femmes
Perez-Felkner, L., Erichsen, K., Li, Y., Chen, J., Hu, S., Ramirez Surmeier, L., et Shore, C. (2024). Computing Education Interventions to Increase Gender Equity from 2000 to 2020: A Systematic Literature Review. Review of Educational Research, 95(3), 536 –580. https://doi.org/10.3102/00346543241241536
Ramanath, S. (2024). The Eve of AI: Reclaiming Womanhood Through Co-Design with Artificial Intelligence. Frontiers in Artificial Intelligence and Applications, 360, 1–9. https://doi.org/10.3233/FAIA240199
Reggiani, M., Gagnon, J. D., et Lunn, R. J. (2024). A holistic understanding of inclusion in STEM: Systemic challenges and support for women and LGBT+ academics and PhD students. Science Education, 108(6), 1637–1669. https://doi.org/10.1002/SCE.21899
Sherman, D. (2016). Encouraging Minority and Low-Income Girls to Pursue Computing through Inclusive Technology Camps. Proceedings of the 2016 ACM SIGMIS Conference on Computers and People Research, (pp. 21–22). ACM. https://doi.org/10.1145/2890602.2906195
Sutton, A., Clowes, M., Preston, L., et Booth, A. (2019). Meeting the review family: Exploring review types and associated information retrieval requirements. Health Information et Libraries Journal, 36(3), 202–222. https://doi.org/10.1111/hir.12276
Tan, S. (2023, 7 juin). Awareness versus Usage of ChatGPT in Australia: how do they vary demographically? https://au.yougov.com/technology/articles/46754-awareness-versus-usage-of-chatgpt-in-australia-how-do-they-vary-demographically?
TechnoCompétences. (2017). La place des femmes dans l’industrie des TI au Québec. Comité sectoriel de la main-d’œuvre en technologies de l’information et des communications. https://www.technocompetences.qc.ca/wp-content/uploads/2018/11/2017_EtudeFemmesenTI_TECHNOComp%C3%A9tences.pdf
TechnoCompétences. (2025). Diagnostic sectoriel 2025-2028 : Portrait de la main-d’œuvre du secteur des technologies de l’information et des communications (TIC) au Québec. Comité sectoriel de main-d’œuvre en technologies de l’information et des communications. https://www.technocompetences.qc.ca/publications/diagnostic-sectoriel-2025-2028-technocompetences/
Ulnicane, I. (2024). Intersectionality in Artificial Intelligence: Framing Concerns and Recommendations for Action. Social Inclusion, 12(0), 7543. https://doi.org/10.17645/si.v12.7543
UNESCO. (2020). Artificial intelligence and gender equality. Key findings of UNESCO’s global dialogue. https://unesdoc.unesco.org/ark:/48223/pf0000374174
UNESCO et International Research Centre on Artificial Intelligence (IRCAI). (2024). Challenging systematic prejudices: An investigation into bias against women and girls in large language models. Paris, France : UNESCO. https://unesdoc.unesco.org/ark:/48223/pf0000388971
Virvou, M., Tsihrintzis, G. A., Phillips-Wren, G., et Jain, L. C. (2022). Special collection of invited original research papers on “Contributions by women in theory and applications of artificial intelligence.” Intelligent Decision Technologies, 17(1), 69–72. https://doi.org/10.3233/idt-230004
West, S. M., Whittaker, M., et Crawford, K. (2019). Discriminating systems: Gender, race, and power in AI. AI Now Institute. https://ainowinstitute.org/publications/discriminating-systems-gender-race-and-power-in-ai-2
Wall, K. (2019). Persévérance et représentation des femmes dans les programmes d’études en STGM. Regards sur la société canadienne. Statistique Canada, no 75-006-X). https://www150.statcan.gc.ca/n1/pub/75-006-x/2019001/article/00006-fra.htm
WomenTech Network. (2025, 28 mai). What Makes Peer-Led Learning Groups So Effective for Women in STEM? https://www.womentech.net/
World Economic Forum. (2023). Global Gender Gap Report 2023 (Insight Report). https://www.weforum.org/publications/global-gender-gap-report-2023/
Young, M. L., Schlesinger, A., et Wang, R. (2023). (Re)Training an Off-the-Shelf Model: Mitigating Gender Biases in AI Through Model Re-Training and Cultural Shift. ACM Transactions on Social Computing, 5(4), Article 37.
Yu, C. (2024). Gender Inequality in the Age of AI: Predictions, Perspectives, and Policy Recommendations. SocArXiv. https://doi.org/10.31219/osf.io/5zrh9