Towards gender equity in AI

Author: Wiktoria Orlowska, DIGITALEUROPE

AI is transforming industries, reshaping job markets, and driving innovation. But what about the gender gap? It would not be a bold assumption to say it exists—even without looking at the data. However, examining the numbers helps us understand how big the gap is and, more importantly, what can be done about it.

And while this article coincides with International Women’s Day, gender equity in AI is not just a once-a-year topic. It is an ongoing issue that affects fairness, opportunity, and the future of technology.

Gender gap in AI: What the data shows

Research confirms that women are underrepresented in AI-related roles and training. A 2024 report from Randstad, Understanding Talent Scarcity: AI & Equity, highlights some key statistics:

• 71% of men say they have AI skills, compared to just 29% of women.
• Women are less likely to use AI at work and less likely to have access to AI tools provided by their employers.
• Women are also less likely to be offered AI upskilling opportunities, putting them at risk of being left behind as AI adoption grows.
• Some countries, like Belgium and India, show promising signs of progress, with more AI training opportunities for women.

The gender gap extends beyond workplace skills. According to the World Economic Forum’s Global Gender Gap Report, women make up only a third of the AI workforce, a figure similar to their representation in STEM fields. In AI and big data courses, women account for just 33% of enrolments, showing that the pipeline for future AI professionals is still male-dominated.

Why does gender equity in AI matter?

The lack of gender diversity in AI is not just a workplace issue—it has real consequences for fairness, opportunity, and the quality of AI itself.

Bias in AI systems

AI is increasingly being used to make decisions that affect people’s lives. If AI teams lack diversity, these systems are more likely to develop biases. For example, facial recognition technology has been found to demonstrate gender and skin-type biases because it was trained on datasets dominated by light-skinned male faces. Having more diverse teams helps ensure AI is fair and works for everyone.

Economic inequality

AI is not just shaping the future—it is defining it. As industries integrate AI into their operations, those without AI skills may find themselves at a disadvantage in the job market. If fewer women are trained in AI, the gender pay gap could widen further, and women may be excluded from high-growth, high-paying roles in emerging technologies.

Missed innovation and market potential

Studies show that diverse teams perform better, drive greater innovation, and make more effective decisions. The lack of gender diversity in AI means we are missing out on perspectives that could lead to more ethical, inclusive, and effective AI solutions. Gender-balanced teams are more likely to anticipate and address unintended biases, leading to better technology for everyone.

Bridging the gender gap: What can be done?

The ARISA AI Skills Strategy for Europe highlights the importance of identifying biases and promoting equity in AI development.

Alongside this, broad efforts are needed to foster gender diversity in AI. Key steps to bridge the gap include:

• Promoting AI as a career choice among underrepresented groups, such as women, to increase participation in AI education and careers.
• Encouraging organisations to implement AI diversity programmes, fostering inclusive recruitment, retention, and leadership development strategies.
• Expanding access to AI education, creating flexible learning pathways that cater to diverse learners, including career changers and those without traditional STEM backgrounds.
• Challenging gender biases in AI content and training data, ensuring that AI systems are built with fairness and equity at their core.

A shared responsibility

Bridging the gender gap in AI requires action from governments, businesses, and society as a whole. Policymakers and educators must ensure AI learning opportunities are open to everyone, while companies need to take real steps towards more inclusive hiring and training. AI is shaping the future, and that future should be built by diverse voices. Promoting gender equity in AI isn’t just about fairness—it also leads to better, more inclusive technology. By increasing access to AI education, fostering diverse workplaces, and addressing biases in AI systems, we can create a more equitable and innovative digital future that benefits everyone. 

Sources:

ARISA Project. (2023). AI Skills Strategy for Europe. https://aiskills.eu/resource/ai-skills-strategy-for-europe-2/

Buolamwini, J., & Gebru, T. (2018). Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification. Proceedings of the Conference on Fairness, Accountability, and Transparency (FAT*), 81, 1–15. https://proceedings.mlr.press/v81/buolamwini18a/buolamwini18a.pdf

Okatta, C. G., Ajayi, F. A., & Olawale, O. (2024). Enhancing Organizational Performance Through Diversity and Inclusion Initiatives: A Meta-Analysis. International Journal of Applied Research in Social Sciences, 6(4), 734–758. https://www.researchgate.net/publication/380115625_ENHANCING_ORGANIZATIONAL_PERFORMANCE_THROUGH_DIVERSITY_AND_INCLUSION_INITIATIVES_A_META-ANALYSIS

Randstad. (2024). Understanding Talent Scarcity: AI & Equity Report. https://www.randstad.com/randstad-ai-equity/

World Economic Forum. (2023). Global Gender Gap Report 2023. https://www.weforum.org/publications/global-gender-gap-report-2023/