I still remember my first day as a software engineering student — one woman in a class of 36, stepping into a field where women weren’t expected to lead, let alone define the future of AI. That experience, though isolating at times, revealed something I carry with me to this day: diversity isn’t just a bonus in engineering — it’s a necessity.
This lesson is more urgent than ever as artificial intelligence becomes the invisible engine behind decisions in hiring, education, and beyond.
According to the World Economic Forum’s Global Gender Gap Report 2023, women make up just 22% of the global AI workforce.¹ This isn’t merely a pipeline problem — it’s a product problem. The people who build AI systems shape how those systems affect society. And when development teams lack diversity, critical perspectives are missing. The consequences can be real — and harmful.
Consider facial recognition technology. Research by the National Institute of Standards and Technology (NIST) found that error rates for women of color were up to 34% higher than for white men.² That’s not a glitch — it’s a structural failure rooted in who’s at the table during design, training, and testing.
I call this “The Missing 78%.” It’s not just about underrepresentation — it’s about the ideas, ethics, and innovations we lose when women aren’t in the room. I introduced this concept during my PyData Global 2024 talk and expanded on it in my Medium article, where I explored how inclusive teams reshape what’s possible with AI.
Women bring not only technical acumen, but also lived experience that helps identify risks, flag edge cases, and prioritize ethical choices — because we’ve lived the gaps ourselves.
Why Diversity Matters in AI
When women are core to engineering teams, we see stronger, safer, and more human-centered outcomes:
Data Justice
We don’t just accept datasets — we interrogate them. Women often lead efforts to ensure data is representative, ethical, and bias-aware.
Explainability
We advocate for transparency — not just for developers, but for the people impacted by these systems.
Human-Centered Design
We build systems that reflect the real world’s complexity, not force people to conform to rigid models.
Inclusive Collaboration
We create environments where diverse voices thrive. The best code is written by teams that listen, challenge, and support one another.
Mentorship and Leadership
Through organizations like SWE and TechWomen, women lift others as they rise. Mentorship isn’t a side effort — it’s a multiplier.
These aren’t “soft skills.” They’re engineering strengths that shape product quality, equity, and impact.
Coding Like a Woman: A Call to Build What’s Next
“Coding it like a woman” isn’t just a phrase — it’s a lineage, a mindset, and a movement. From Ada Lovelace’s pioneering algorithm to Mira Murati’s leadership at OpenAI, to Marissa Mayer’s product innovations at scale — women have long shaped technology. Now, we’re called to lead the next era of AI.
Here’s how we show up:
Question the Defaults — Like Ada Did
Ada Lovelace imagined computing before it existed. Today, we must ask: Who benefits? Who’s left out?
Build Ethics Into the Core — Like Mira Leads
Mira Murati’s work reminds us that responsibility must be built in — not bolted on. Women engineers are leading that shift.
Design for Impact — Like Marissa Delivered
Marissa Mayer helped scale products used by billions. Let’s use that same clarity to ensure AI systems work for everyone.
Mentor with Multipliers in Mind
Mentorship through SWE, TechWomen, or local communities builds not just careers, but ecosystems of equity.
Shape the Rules of the Game
AI governance needs voices from the field. Let’s not just respond to policy — let’s create it.
Building What’s Missing
AI is shaping the tools we use and the roles we hold. Yet LinkedIn’s Economic Graph shows women are underrepresented in AI-enhanced roles (25.8% vs. 31.6% for men) and overrepresented in jobs most vulnerable to automation.³ This isn’t just a workforce issue — it’s a future issue.
At Amazon, I’ve seen how diverse teams fuel innovation at global scale. As chair of the Code of Conduct Working Group at NumFOCUS, I’ve worked to ensure open-source AI communities remain inclusive and values-driven. These aren’t just ideals — they’re engineering imperatives. And they’re possible when we lead together.
So, if you’ve ever wondered whether your voice belongs in AI, it does. Not just as a user. Not just as a contributor. But as a builder.
Let’s code it like a woman — and engineer a future that includes us all.
Sources
- World Economic Forum. Global Gender Gap Report 2023.
- NIST. Face Recognition Vendor Test (FRVT) Part 3: Demographic Effects.
- LinkedIn & World Economic Forum. Jobs of Tomorrow: Large Language Models and Jobs. 2023.
