While the presence of women in STEM (Science, Technology, Engineering, and Mathematics) has made significant progress, the advent of artificial intelligence (AI) presents us with new challenges—both formidable and exciting. The inclusion of women is no longer merely a matter of social justice or economic performance; it has become an ethical imperative to ensure that the AI of tomorrow does not reproduce past biases.
Progress Achieved: The Result of Concrete Actions and Stakeholder Collaboration
Just a few years ago, gender diversity in the ICT sector was a glaring issue. Today, major advances have been made through exemplary tripartite cooperation among governments, businesses, and academic institutions.
Public authorities have played a key role by promoting girls’ access to scientific and technological careers from an early age. National awareness campaigns, such as certain government initiatives in France and Canada, highlight inspiring female role models, break stereotypes, and demonstrate to young girls that careers in tech are within their reach.
Meanwhile, companies in the sector have heavily invested in mentoring programs, targeted training, and more inclusive recruitment policies. The “Women in Tech” initiative exemplifies these efforts. Partnerships with universities and engineering schools are crucial. By collaborating with these institutions, companies can influence curricula, offer paid internships, and sponsor events to attract female students. The example of the Women and Science Chair at École Polytechnique in France is particularly illustrative, demonstrating how academia and the private sector can unite to promote women’s scientific careers.
The economic benefits are undeniable. A recent McKinsey analysis revealed that the most gender-diverse companies in their leadership teams are 25% more likely to generate above-average profitability. Similarly, Boston Consulting Group (BCG) demonstrated that mixed leadership teams achieve 19% higher innovation revenues. These figures underscore the tangible added value of diverse perspectives. While these progress points are encouraging, the rapid evolution of AI necessitates accelerating this momentum.
The Major Challenge of AI: Preventing Bias Reproduction
AI reflects its creators—and, most importantly, the training data it is fed. If development teams are homogeneous and the data used is historically biased, there is a risk of building systems that perpetuate or even amplify gender inequalities. The example of Amazon’s recruitment tool, which ultimately favored male profiles, serves as a stark warning. An AI without diversity is a high-risk AI.
To overcome this obstacle, it is imperative to embed gender equity into the design and governance of these systems. This involves shifting from mere “bias correction” at the end of the process to a proactive, systemic approach:
- Gender-sensitive impact assessments: Rigorous evaluations of the potential impact of high-risk AI systems on fundamental rights are essential before deployment. These assessments should specifically analyze risks of discrimination against women and marginalized groups, considering intersectional factors such as age, race, or social status.
- Audits of data diversity: Transparency is key. Independent audits of training datasets are crucial to verify that data accurately represent the diversity of populations. Correcting imbalances upstream is vital to prevent algorithmic discrimination.
- Promoting diverse development teams: Diversity begins with design teams. Encouraging the participation of women and minorities in tech roles ensures a broader range of perspectives from the outset. This is the most effective way to identify and mitigate biases before they manifest in algorithms.
Going Further: New Initiatives Are Needed
To sustainably transform the sector, deeper engagement is required. It is no longer enough to attract women; organizations must also support their advancement within the workplace.
Companies should go beyond rhetoric by implementing transparent pay equity policies. Publishing data on wage gaps and publicly committing to reducing them would be highly relevant. Additionally, reverse mentoring programs can create value by enabling young female developers to train senior leaders on emerging technologies and inclusion issues.
Finally, addressing the ethical challenges of AI necessitates the creation of diverse ethical committees. Composed of varied profiles—engineers, designers, sociologists, and ethics specialists—these committees would oversee data sets and potential algorithmic biases. Initiatives such as the Global Partnership on AI (GPAI) demonstrate the path toward more inclusive and responsible AI governance.
Conclusion: An Imperative for an Inclusive Digital Future
The progress made so far demonstrates our collective capacity to adapt and advance. However, the rapid development of AI calls for even stronger commitment. By placing women’s inclusion at the core of AI design, development, and governance, the sector will not only promote equality but also build more reliable, robust, and fair technologies. Making diversity a driving force of daily innovation—rather than just an objective—will enable the industry to forge a truly prosperous technological future for all.
