Women and technology: overcoming stereotypes, valuing skills

The barriers that remain

• Technical knowledge: many women report that they have not received specific training on AI and do not feel confident using it beyond basic operations.
• Fear of making mistakes or being judged: impostor syndrome often manifests itself more strongly when it comes to technology perceived as “difficult”.
• Uncurated data / poorly documented processes: if you don't already have a clean, consistent, orderly database, AI can give inaccurate or useless answers. And this can reinforce the feeling of not being up to the task.   
• Culture and organizational models that reward “going it alone,” hyper-competition, speed in releasing new models and tools, and quantity over quality, while soft skills remain undervalued.

A different approach: the value of soft skills

What happens when tech approaches and human approaches truly meet? Different, more authentic, and more sustainable ways are born.

Our “non-technical” skills make a difference

• Empathyunderstanding the people who interact with AI, imagining scenarios, understanding unintended impacts.
• Collaborationworking in multidisciplinary teams (technical and non-technical), facilitating dialogue between those who design algorithms and those who “live” the effect of AI.
• Systemic visionseeing beyond the specific task: how that AI influences other processes, workflows, people, and corporate culture.
• Clear communication and public outreachtranslate technical language into terms that everyone can understand, making risks and opportunities clear.

Ethical AI: the Immanence case in Italy 

Immanence it's an example that feels very close to me: a company that works with AI by placing ethics, governance, and social impact at its core. And it is a great satisfaction for me to be able to say that today it is also part of Decentralized, the business consortium to which Flowerista belongs.

It is led by two women, Luna Bianchi and Diletta Huyskes, with a clear mission on ethics and governance. It supports public and private organizations on a decisive path: that of making innovation an engine of positive change, capable of respecting people, rights, and contexts.

Through evaluation practices, governance, and applied ethics, Immanence works side by side with those who develop and adopt digital tools and artificial intelligence, helping to guide choices based on clear principles: fairness, transparency, and accountability. It does not limit itself to assessing systems, but also looks at the teams that design them and the conditions in which they operate, because technology is never neutral: it reflects values, biases, and priorities.

Their approach makes it possible to move from a naive vision of AI — made only of promises and performance — to a conscious use, where automation becomes an ally and not a replacement, and where social impact is an integral part of technological success.

This is confirmation that you do not need to start with a technical degree: you need to bring a sensibility, a question, a method.

International contexts: what we can see on the horizon

Looking elsewhere does not mean imitating, but rather drawing inspiration and taking cues.

What is happening in China

• It is one of the countries where tech is growing the fastest, digital industries are agile, and startups are encouraged at both local and national levels.
• According to the report of CESE-M, female participation in Chinese tech is rising: women working in software development, digital products, and operational management, not just in marketing or “support” roles.
• Industrial policies that put innovation at the center, accessible STEM education, and state orientations toward digitalization, AI, and infrastructure make it an interesting laboratory of what can happen when resources, training, and female aspirations find room.

What is happening in the United States

• • Come, leader Mira Murati (formerly OpenAI and now leading Thinking Machines Lab), Daniela Amodei (Anthropic) these are rare cases, but they show that it is possible to be at the technical / product top even in companies that define the global AI landscape.
  • Activism on bias, algorithmic justice (Joy Buolamwini), social impact (Kate Crawford), training/inclusion (Tess Posner, Sinead Bovell) are emerging as structural components of the AI discourse.
    Let's see who is advancing these issues in the next paragraph. 

Women driving global AI 

Here are six women to follow on the global scene:  

Fei-Fei Li

Fei-Fei Li is one of the scientists who made the current AI revolution possible. With the project ImageNet it has allowed machines to recognize objects and visual scenes, paving the way for deep learning. At Stanford she leads the’Institute for Human-Centered AI, where it promotes an artificial intelligence built around human values. With AI4ALL bring this knowledge into schools and among underrepresented communities, creating new opportunities for young people and girls.
Fei-Fei Li is not only a world-renowned scientist, but also a visionary who has always repeated: “AI must be human-centered”. 

Mira Murati

Mira was born in Albania and grew up in Canada. Her passion for engineering leads her to Tesla, then to OpenAI. When the world discovers ChatGPT, she is the CTO who led the development and who must face the hardest questions: what does it mean to release such a powerful technology to the public? Her figure shows that one can be at the center of the global debate without compromising one's integrity, bringing the perspective of those who see AI not just as a product but as a collective responsibility.

Daniela Amodei

Italian, daughter of immigrants, she begins her career far from tech, in the non-profit sector and advocacy. Then the leap: from OpenAI to co-founding Anthropic, the company that has made “safety” its core. Daniela often recounts that her goal is not to create the fastest AI, but the safest one. Her journey shows that even those coming from non-technical backgrounds can have a decisive impact, bringing ethical and social sensitivities into the heart of technology.

Joy Buolamwini

Joy grew up between Ghana and the United States. While studying at MIT, she realizes that facial recognition software does not “see” her features: the models were not trained on Black faces. From that personal experience comes her battle: she founds the Algorithmic Justice League, she becomes an activist and author, bringing the complaint of algorithmic bias before the US Congress. Her story reminds us that change often starts from an intimate, almost painful episode, transformed into a public mission.

Kate Crawford

Kate does not come from engineering, but from sociology and media studies. Her strength is looking at AI from the outside, dismantling its myths, and revealing its hidden implications. In her books and lectures, she explains how AI is not “immaterial,” but made of mines, server farms, and invisible human labor. She is the voice that warns us: innovation is not neutral, and behind every algorithm there are bodies, territories, and resources. Her work is a constant invitation to look beyond the surface.

Sinead Bovell

Canadian model, finance graduate, could have stayed on the runway. Instead, she chooses to become a “futurist,” founding WAYE, an organization that teaches young people — especially women and minorities — how to interact with new technologies. Sinead brings her charisma and voice to contexts where tech discussions often sound cold and distant. Her message is simple and powerful: AI concerns everyone, and no one must be left out.

So even we, non-STEM women, can work with AI today

Let's not talk about abstract concepts: there are simple, concrete actions that each of us can start putting into practice right away, without the need for large investments of time or resources, but with the desire to experiment.

1) Learning by doing — small experiments

• Try a project every week: use ChatGPT for a challenging email, to create a presentation, to summarize meeting notes. Write your prompts, see what changes with a few variations.
• Try related tools — for example, transcription tools, PDF summaries, image generation (if you need them). We talked about this at length in this article on our blog. 

2) Organise mental space, data and processes

• Check your starting data: are they clean? Are they organized? Do you have versions, backups, and clear boundaries of what you are using?
• Map 2-3 processes in your work that cost you time or generate repeated errors, which you could delegate or automate with AI or digital tools.

3) Building communities and supporting networks

• Compare yourself with other people, don't be afraid to ask! 
• Attend or organize workshops/meetups with colleagues and friends; share real-life cases, failures, successes.

4) Wondering about the purpose, context, and impacts

• For every new tool, it is worth asking: “What problem does it solve? Who loses if it is misused?”
• Watch out for bias, to transparency, to the protection of personal data.

5) Continuous training

• Dedicate study time (even briefly, maybe half an hour) to understand how you can use a new tool: prompts, limits, ethical scenarios.
• Take advantage of free or low-cost resources, there are tons of them online, especially on YouTube! 

Future prospects: what we can hope for, ask for, build

If we broaden our view beyond individual personal practices, broader scenarios emerge that can truly make a difference.

We need public policies that they look at AI with a gender perspective, that they do not limit themselves to regulating innovation but that they support training, incentives, and inclusive paths for those who today risk being left on the margins.

Of course they are needed companies and organizations capable of integrating AI governance into their business models: not thinking of AI solely as a tool to be used, but as a system that brings with it values, visions, and responsibilities. We discussed this who, together with Redlab, and we will continue to do so.

If we look at capital and funding, The point is not only the availability of resources, but the criteria by which they are distributed. Today, the world of venture capital and startup investments continues to almost exclusively favor short-term financial returns, while everything concerning governance, ethics, and social impact remains in the background.

Yet, the very cases we have cited show that female-led initiatives in AI share a common characteristic: they bring together technological expertise and ethical sensitivity, a focus on sustainability, and a capacity for long-term vision. Valuing this type of approach would mean opening up to a process of cultural change, not just a market one.

It is a longer and deeper path, which requires revising the very way we judge innovation: not only based on how fast it scales or how much return it generates, but also on how much it contributes to building fairer, more inclusive, and responsible systems.

And finally there are the networks and communities: in Italy there are already precious experiences, from Women in AI a SheTech down to smaller, local initiatives that create spaces for discussion and mutual support. Strengthening them and getting them to talk to each other means building common ground where knowledge multiplies and truly becomes a collective asset.

Conclusion: from curiosity to collective action

What we have described is not a path that is walked alone. It is a process that grows when we share – doubts, mistakes, successes. When we recognize that the value we bring is not “inferior,” but different and complementary.

Let's start now: choose a concrete experiment, do it together with someone, share it. That way, AI won't just be a promise: it will become a tool that we use with awareness, ethics, and real power.