AI is rapidly changing the way organizations work and make decisions. Yet women’s influence in AI development remains limited. As in other tech professions, women are underrepresented in data science, machine learning, and algorithm development. This is even though practice regularly shows that diverse teams build better models, recognize bias more quickly, and develop solutions that are more relevant to a broad group of users. In a recent roundtable discussion we hosted with Visma, experts discussed how women can play a key role in shaping AI.
Figures show that young women are still less likely to choose technical studies that lead to a career in AI. Cultural expectations, stereotypes, and a lack of visible role models play a significant role in this. During the discussion, participants emphasized that women in AI should not only become technology experts, but also be visible as leaders and inspirers. Mentoring, networking, and sharing experiences are crucial to attracting and retaining talent. This will enable women to enter AI roles, grow sustainably, and influence the strategy and development of this technology.
The value of social intelligence in AI
While discussions about AI often focus on data, algorithms, and computing power, the social context in which technology operates is equally important. After all, AI systems reflect the values, assumptions, and decisions of the people who build them. It is precisely at this intersection between technology and society that women can play a crucial role, as they often bring strong social and communication skills that help to understand better and steer the impact of AI.
Irne Verwijst, AI & Data Lead at Visma Circle, sees this as an important opportunity for women to make a difference. “An important part of AI is actually the social aspect,” she says. “Women often have a certain social affinity, which is precisely why they can respond to this topic and make a contribution that may have more impact than men do. It’s not just about technical knowledge or programming, but also about understanding the social context and how technology is used.” According to her, this enables women to identify ethical dilemmas or blind spots earlier on, thereby helping to develop AI in a responsible manner.
Based on this idea, companies that harness the power of diversity can build a system that is not only technically strong but also better aligned with society. Through their social awareness, empathy, and attention to nuance, women can contribute to AI that is fairer, more inclusive, and more reliable. This applies, for example, to systems that allocate healthcare or to educational platforms, where unconscious bias can have major consequences.
The path to fair AI
After all, algorithms learn from data that is collected and interpreted by humans. If the composition of development teams is too one-sided, unconscious biases creep into the model, with all the consequences that entails. Different backgrounds, perspectives, and experiences help to reduce those blind spots. Diversity is therefore a choice to make AI systems fairer, more reliable, and more applicable.
Véronique Van Vlasselaer, Analytics & AI Lead at SAS, emphasizes that women can play a particularly valuable role in this. “I think diversity and women are very important in the development of AI systems. If you are in a minority group, you are more likely to see where systems fall short or where certain groups are disadvantaged. My experience in social security fraud shows that women or people from different backgrounds are often assessed differently by systems, simply because their perspective is missing from the data or the design. By bringing these different perspectives together, AI systems can operate more creatively and fairly. Ultimately, I believe that if we as developers work together effectively, we can remove social bias from AI and build systems that are fairer for everyone, regardless of gender, color, or background.”
By uniting different perspectives, organizations can correct existing bias or even prevent it from arising in the first place. Think of recruitment algorithms that no longer show a preference for a particular gender.
From hype to human innovation
In addition to the need for diversity and inclusion, there is also an opportunity to use the current AI hype as a catalyst for change. AI arouses curiosity, even among people who do not immediately consider themselves techies. According to Joyce Datema, owner of joycedatema.nl and initiator of the AI Café, this presents an opportunity to involve more women in technological innovation. “AI is a tool, not an end in itself, and that’s what makes it interesting,” says Datema.
She sees AI as a tremendous opportunity to use technology on the human side, to improve processes and tackle social problems. “By consciously deploying women in this field, they can enter the market more quickly and make an impact. The hype surrounding AI can serve as a motivator to involve more women in technology, because it shows that there is much to discover and that their perspectives are valuable. It is an invitation not only to follow traditional roles, but to actively participate in technological innovation and help build solutions that matter.”
By emphasizing social applications, AI becomes more accessible and relevant to a wider audience. Women from fields such as healthcare, communication, education, or policy can use their experience to bring AI projects closer to reality. In this way, technology becomes something that is developed with people in mind. This shift from technology to impact makes the field more attractive and shows that a career in AI is also about creativity, collaboration, and social engagement.
Inclusive data as a foundation
You often hear that a model is only as good as the data it is trained on. For fair and representative AI, attention must be paid to the quality and origin of datasets. Many datasets have been shaped by history and are therefore anything but neutral, with direct implications for the outcomes of AI systems.
Lieke Hamers, Field CTO at Dell Technologies Netherlands, sees this as a structural problem: “The problem with AI and data is that historical data feed algorithms, and that data is often skewed. Many systems are trained on images or data from white men, which means that women and people of color are less well recognized or assessed. This also applies to medical data. We have too little information about women. And even less about women of color. Suppose we want AI to work effectively for everyone. In that case, we need to tackle that problem at the source by collecting more inclusive datasets and getting more female scientists to actively participate in research.”
Inclusive data is a moral and social necessity in this regard. When AI is fed with one-sided information, it reinforces existing inequalities rather than reducing them. Examples include medical algorithms that are less effective at recognizing diseases in women, or recruitment systems that unconsciously favor male profiles. By ensuring balanced data collection as early as the research phase, developers can prevent this imbalance from translating into discrimination in practice. This requires awareness, care, and collaboration between technicians, scientists, and policymakers.
When women actively participate in determining what data is collected, how it is interpreted and applied, they can contribute to AI that reflects everyone. With their perspectives, social intelligence and experience from various sectors, women help organisations build fair and reliable systems. Visibility and active participation in technological projects are crucial in this regard. The combination of technical expertise and the ability to understand and steer the social impact of AI results in meaningful models and systems that benefit society.
Towards a future of equal opportunities
The story of women in AI shows that their influence goes beyond the code they write or the models they train. Their input helps determine how ethical choices are made, how social impact is taken into account, and how systems become fairer. Organizations that recognize these insights and actively include women in AI projects benefit from broader perspectives, greater innovation, and better decision-making.
This is a call to action for companies, educational institutions, and policymakers: encourage girls and young women to pursue technical studies and highlight the social relevance of AI. This will create an environment in which diversity leads to technology that is more equitable and effective.
This was our last story in a series about women in technology. Read the first story about women in tech and the second story about women in cybersecurity.