DeepTech Still Gets Women Wrong – We’re Fixing ThatBias is still baked into AI and DeepTech careers. Here’s what needs to change — and how we can lead the charge.

🌟 Introduction: The Promise and the Problem DeepTech — the frontier of artificial intelligence, robotics, biotechnology, and cloud computing — promises to reshape our world. It’s where breakthroughs happen, where science meets engineering, and where the future is being built. But beneath the glossy headlines and billion‑dollar valuations lies a persistent problem: bias is still baked into AI systems and deep tech careers. Women remain underrepresented, underestimated, and often overlooked in the very fields that claim to be building a fairer, smarter future. The irony is stark. DeepTech is supposed to be about solving humanity’s hardest problems. Yet it continues to replicate the oldest one: inequality.

🚧 Bias in AI: When Machines Mirror Society’s Flaws. Artificial intelligence is often described as objective, rational, and free from human prejudice. But in reality, AI systems are only as fair as the data they’re trained on. • Facial recognition failures: Studies have shown that AI misidentifies women and people of color at far higher rates than white men. • Hiring algorithms: Some recruitment AIs have been caught downgrading CVs with women’s names or penalizing career breaks often associated with caregiving. • Healthcare AI: Diagnostic tools sometimes fail to account for gender differences in symptoms, leading to misdiagnosis or underdiagnosis for women. These failures aren’t just technical glitches. They’re reflections of systemic bias. And unless women are part of designing, testing, and governing these systems, the cycle will continue.

📚 Bias in Careers: The Invisible Ceiling in DeepTech Bias isn’t limited to algorithms. It’s embedded in the careers that build them. • Representation gaps: Women make up less than 20% of AI researchers globally. In robotics and cloud architecture, the numbers are even lower. • Leadership barriers: Women in deep tech often hit invisible ceilings, are excluded from decision‑making roles or are overlooked for funding. • Recognition gaps: Contributions by women are frequently minimised. Male colleagues are more likely to be cited, promoted, or celebrated in the media. This isn’t just unfair — it’s inefficient. Diverse teams consistently outperform homogeneous ones. By sidelining women, deep tech is slowing its own progress.

🌍 Why This Matters: Bias in deep tech isn’t just a “women’s issue.” It’s a human issue. When half the population is excluded or misrepresented, the technology we build is incomplete. • AI that misidentifies women → undermines trust in public safety tools. • Robotics designed without women’s input → ignores accessibility and caregiving applications. • Cloud systems built by homogeneous teams → risk overlooking diverse user needs. If deep tech is to solve humanity’s hardest problems, it must first solve its own bias problem.

💡 What Needs to Change

1. Data Diversity AI systems must be trained on datasets that reflect the full spectrum of humanity. That means including women, people of colour, neurodivergent individuals, and underrepresented communities.

2. Inclusive Design: Women must be part of the design process from the start. Not as token voices, but as leaders shaping the vision.

3. Career Pathways: Deep tech companies must create transparent pathways for women to enter, grow, and lead. That includes mentorship, sponsorship, and fair evaluation systems.

4. Funding Equity Female founders in deep tech receive a fraction of venture capital compared to men. Closing this gap is essential to unlock innovation.

5. Cultural Shift: We need to normalise failure, experimentation, and risk‑taking for women in tech. Too often, women are penalised for mistakes that men are allowed to learn from.

🚀 How Women Can Lead the Charge

1. Building Communities Networks like WomenTech, TechSheThink, and grassroots collectives provide spaces for women to share resources, mentor each other, and amplify voices.

2. Owning Narratives: Women must tell their own stories — through blogs, podcasts, conferences, and lesson packs. Visibility matters.

3. Policy Advocacy Push for regulations that demand fairness in AI, transparency in algorithms, and accountability in hiring practices.

4. Entrepreneurship, Women founders in deep tech can redefine the industry by building companies rooted in inclusivity. Microproducts, lesson packs, and AI‑powered tools can scale globally while embedding diversity at their core.

5. Mentorship & Sponsorship Senior women in tech must mentor younger ones, while allies sponsor women into leadership roles. This dual approach accelerates change.

📚 Case Studies: Women Fixing DeepTech

• Joy Buolamwini: Founded the Algorithmic Justice League to fight bias in AI.

• Ayanna Howard: Pioneered human‑robot interaction research, making robotics more inclusive.

• Fei‑Fei Li: Advocated for “AI for All,” ensuring diverse participation in AI development.

• Cloud architects in major firms: Women engineers have led resilience design after outages, proving that inclusion strengthens infrastructure. These leaders show that women aren’t just participating in deep tech — they’re transforming it.

🔮 The Future We’re Building Imagine a future where: • AI systems recognise every face accurately. • Robotics assist caregivers and empower children with disabilities. • Cloud infrastructures serve diverse communities seamlessly. • Women lead deep tech companies, shaping innovation at the highest levels. This isn’t utopia. It’s achievable — if we dismantle bias and build inclusivity into the DNA of deep tech.

🌟 Conclusion: Fixing DeepTech by Fixing Bias. DeepTech still gets women wrong. But we’re fixing that. Bias in AI and careers is not inevitable. It’s a design flaw. And design flaws can be corrected. By diversifying data, redesigning systems, and empowering women to lead, we can build a deep tech industry that truly reflects humanity. The strongest breakthroughs don’t come from perfect systems. They come from imperfect ones that learn, adapt, and evolve. DeepTech must do the same. Women are not waiting to be included. We are leading the charge. And when we fix bias in deep tech, we don’t just fix technology — we fix the future.