To bring about lasting change in the sector, a deeper commitment is needed. It is no longer just a question of attracting women, but also of enabling them to advance within organizations.
Claire Khoury, Director Marketing, Communication & CSR, Sofrecom
While women’s presence in STEM fields has made notable progress, the advent of artificial intelligence (AI) presents us with new, formidable yet exciting challenges. Inclusion of women is no longer just a matter of social justice or economic performance; it has become an ethical imperative to ensure that tomorrow’s AI does not reproduce past biases.
Progress Made: The result of concrete actions & Stakeholder cooperation
Just a few years ago, gender diversity in the ICT sector was a glaring issue. Today, thanks to exemplary tripartite cooperation among governments, businesses, and academic institutions, significant advances have been achieved.
Public authorities have played a key role in encouraging girls to pursue scientific and technological careers from an early age. National awareness campaigns, such as government initiatives in France and Canada, highlight inspiring female role models, dispel stereotypes, and demonstrate to young girls that technology careers are accessible to them.
Meanwhile, companies in the tech sector have invested heavily in mentoring programs, specialized training, and more inclusive recruitment policies. The “Women in Tech” initiative is an example of these efforts. Partnerships with universities and engineering schools are also essential. Through these collaborations, companies can influence curricula, offer paid internships, and sponsor events to attract female students. The Women and Science Chair at École Polytechnique in France is a prime example of how academia and the private sector can collaborate to promote women’s careers in science.
On the economic front, the evidence is irrefutable. A recent McKinsey analysis revealed that the most gender-diverse companies in their leadership teams are 25% more likely to generate above-average profitability. The Boston Consulting Group (BCG) also demonstrated that mixed leadership teams achieve 19% higher innovation revenues. These figures highlight the tangible added value of diverse perspectives. While these progress points are solid, the horizon of AI demands accelerating this momentum.
The grand challenge of AI: Preventing bias reproduction
AI reflects its creators and, above all, its training data. If development teams are homogeneous and the data used are historically biased, there is a risk of building systems that perpetuate or even amplify gender inequalities. The example of Amazon’s recruitment tool, which ended up favoring male profiles, is a stark warning. An AI without diversity is a high-risk AI.
To overcome this obstacle, it is essential to embed gender equity into the design and governance of these systems. This involves moving from simple “bias correction” at the end of the process to a proactive and systemic approach:
- Impact assessments considering gender: Before deploying any high-risk AI system, rigorous evaluations of its impact on fundamental rights are necessary. These should specifically analyze risks of discrimination against women and marginalized groups, considering intersectional factors such as age, race, or social status.
- Data diversity audits: Transparency is key. Independent audits of training datasets are crucial. These audits must verify that data are representative of the diversity of populations. Correcting imbalances upstream is vital to avoid algorithmic discrimination.
- Promoting diverse development teams: Diversity begins with design teams. Encouraging the participation of women and minorities in tech careers ensures a broader range of perspectives from the outset. This is the best way to detect and mitigate biases before they manifest in algorithms.
Going Further: New initiatives are needed
To sustainably transform the sector, deeper engagement is required. It’s no longer just about attracting women but also about enabling their advancement within organizations.
Companies should go beyond rhetoric by implementing transparent pay parity policies. Publishing data on wage gaps and publicly committing to reducing them would be relevant. Additionally, reverse mentoring programs can create value by allowing young female developers to train more experienced leaders on new technologies and inclusion issues.
Finally, to address the ethical challenge of AI, creating diverse ethical committees is a non-negotiable step. Composed of varied profiles—engineers, designers, sociologists, and ethics specialists—these committees would oversee data sets and potential algorithmic biases. Initiatives like 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 demonstrates the collective ability to adapt and improve. But the rise of AI calls for even stronger commitment. By placing women’s inclusion at the heart of AI design, development, and governance, the sector will not only advance equality but also build more reliable, robust, and fair technologies. Making diversity not just a goal but a driving force of daily innovation will enable the industry to create a truly prosperous technological future for all.
