"We cannot fight bias in AI with biased teams." - Joy Buolamwini If you're reading this, chances are you've asked ChatGPT a question, used a recommendation system to binge your next favourite show, or watched an AI filter turn your selfie into a Renaissance painting. But while we marvel at the power of artificial intelligence, there's a question we don’t ask enough: Who helped build this intelligence? Behind the neural nets and billions of parameters, there’s a less-visible yet incredibly vital force: women-scientists, engineers, ethicists, and pioneers, who have shaped the foundations and ethics of AI as we know it today. Ada Lovelace: The Original Architect of Algorithms Let’s rewind. While AI might seem like a modern marvel, its roots can be traced back to the 1800s-yes, really. Ada Lovelace, often dubbed the world’s first computer programmer, wrote what’s considered the first algorithm intended for a machine. She imagined a future where machines could do more than just crunch numbers. Sounds familiar? Lovelace didn’t build AI, but she cracked open the door for computational thinking. AI walked through it. Fei-Fei Li: Teaching AI to See No conversation about modern AI is complete without Dr. Fei-Fei Li, co-director of Stanford’s Human-Centered AI Institute and the creator of ImageNet, a dataset so massive and so detailed that it helped birth the modern computer vision revolution. Before ImageNet, AI struggled with image classification. Today, your phone can distinguish between a cat and a cappuccino, thanks to deep learning trained on the very data her team curated. Fei-Fei has also been a staunch advocate for ethical, inclusive, and human-centered AI, reminding the world that intelligence means nothing without empathy. Timnit Gebru: The Voice of Conscience AI is powerful, but power without accountability is dangerous. That’s where Timnit Gebru steps in. A computer scientist and former co-lead of Google’s Ethical AI team, Gebru’s research on algorithmic bias and facial recognition revealed how AI can perpetuate racial and gender discrimination. Her work didn’t just expose flaws, it triggered global conversations about ethics, corporate responsibility, and diversity in AI teams. Timnit’s exit from Google made headlines, but it also sparked a movement, pushing tech companies to confront uncomfortable truths. Joy Buolamwini: Fighting Bias with the Coded Gaze You’ve likely heard of facial recognition. But did you know some of these systems failed to accurately identify darker-skinned women? Joy Buolamwini, founder of the Algorithmic Justice League, brought this to light through her research at MIT Media Lab. Her documentary Coded Bias peeled back the shiny facade of AI to reveal something alarming: unintentional biases baked into code, stemming from a lack of representation in training data and development teams. Joy’s advocacy has led to real-world policy changes and increased scrutiny on AI deployments in law enforcement and government. Other Women Changing the Game Rana el Kaliouby, a pioneer in affective computing, built emotion AI that can detect human feelings through facial expressions. Cynthia Breazeal, one of the first to work on social robotics, made machines more interactive and human-like. Kate Crawford, whose work spans the political and philosophical dimensions of AI, co-founded the AI Now Institute to make tech more equitable. These aren’t token names. These are women rewriting the rules of what machines can learn, feel, and decide. Why This Matters? The global AI narrative has been shaped for too long by a narrow demographic. When AI is designed without diversity in its creators, it risks reinforcing harmful stereotypes and overlooking marginalized communities. As AI integrates deeper into everything, from healthcare diagnostics to financial lending, it becomes a social responsibility to ensure that its architects reflect the world it’s meant to serve. Yet women still make up only about 22% of AI professionals globally, according to the World Economic Forum. That gap isn’t just about representation-it’s a blind spot in innovation, ethics, and empathy. A Call to Action If you’re in tech: mentor a woman. If you’re hiring: diversify your team. If you’re building products: question your data. If you’re writing AI policy: listen to the voices that have been there since day one but haven’t always had the mic. The future of AI is not just about what machines can do-it’s about who gets to shape that future. Let’s make sure it’s a future built by everyone, for everyone. "We cannot fight bias in AI with biased teams." - Joy Buolamwini "We cannot fight bias in AI with biased teams." - Joy Buolamwini "We cannot fight bias in AI with biased teams." If you're reading this, chances are you've asked ChatGPT a question, used a recommendation system to binge your next favourite show, or watched an AI filter turn your selfie into a Renaissance painting. But while we marvel at the power of artificial intelligence, there's a question we don’t ask enough: Who helped build this intelligence? Who helped build this intelligence? Who helped build this intelligence? Behind the neural nets and billions of parameters, there’s a less-visible yet incredibly vital force: women -scientists, engineers, ethicists, and pioneers, who have shaped the foundations and ethics of AI as we know it today. women Ada Lovelace: The Original Architect of Algorithms Ada Lovelace: The Original Architect of Algorithms Let’s rewind. While AI might seem like a modern marvel, its roots can be traced back to the 1800s-yes, really. Ada Lovelace, often dubbed the world’s first computer programmer , wrote what’s considered the first algorithm intended for a machine. She imagined a future where machines could do more than just crunch numbers. world’s first computer programmer Sounds familiar? Lovelace didn’t build AI, but she cracked open the door for computational thinking. AI walked through it. Fei-Fei Li: Teaching AI to See Fei-Fei Li: Teaching AI to See No conversation about modern AI is complete without Dr. Fei-Fei Li , co-director of Stanford’s Human-Centered AI Institute and the creator of ImageNet, a dataset so massive and so detailed that it helped birth the modern computer vision revolution. Dr. Fei-Fei Li Before ImageNet, AI struggled with image classification. Today, your phone can distinguish between a cat and a cappuccino, thanks to deep learning trained on the very data her team curated. Fei-Fei has also been a staunch advocate for ethical, inclusive, and human-centered AI, reminding the world that intelligence means nothing without empathy. ethical, inclusive, and human-centered AI, Timnit Gebru: The Voice of Conscience Timnit Gebru: The Voice of Conscience AI is powerful, but power without accountability is dangerous. That’s where Timnit Gebru steps in. A computer scientist and former co-lead of Google’s Ethical AI team, Gebru’s research on algorithmic bias and facial recognition revealed how AI can perpetuate racial and gender discrimination. Timnit Gebru Her work didn’t just expose flaws, it triggered global conversations about ethics, corporate responsibility, and diversity in AI teams. Timnit’s exit from Google made headlines, but it also sparked a movement, pushing tech companies to confront uncomfortable truths. ethics, corporate responsibility, and diversity in AI teams. Joy Buolamwini: Fighting Bias with the Coded Gaze Joy Buolamwini: Fighting Bias with the Coded Gaze You’ve likely heard of facial recognition. But did you know some of these systems failed to accurately identify darker-skinned women? Joy Buolamwini , founder of the Algorithmic Justice League, brought this to light through her research at MIT Media Lab. Joy Buolamwini Her documentary Coded Bias peeled back the shiny facade of AI to reveal something alarming: unintentional biases baked into code, stemming from a lack of representation in training data and development teams. Joy’s advocacy has led to real-world policy changes and increased scrutiny on AI deployments in law enforcement and government. Coded Bias peeled back the shiny facade of AI to reveal something alarming: unintentional biases baked into code, stemming from a lack of representation in training data and development teams. Coded Bias Other Women Changing the Game Other Women Changing the Game Rana el Kaliouby, a pioneer in affective computing, built emotion AI that can detect human feelings through facial expressions. Cynthia Breazeal, one of the first to work on social robotics, made machines more interactive and human-like. Kate Crawford, whose work spans the political and philosophical dimensions of AI, co-founded the AI Now Institute to make tech more equitable. Rana el Kaliouby , a pioneer in affective computing, built emotion AI that can detect human feelings through facial expressions. Rana el Kaliouby emotion AI Cynthia Breazeal , one of the first to work on social robotics , made machines more interactive and human-like. Cynthia Breazeal social robotics Kate Crawford , whose work spans the political and philosophical dimensions of AI, co-founded the AI Now Institute to make tech more equitable. Kate Crawford make tech more equitable. These aren’t token names. These are women rewriting the rules of what machines can learn, feel, and decide. These aren’t token names. These are women rewriting the rules of what machines can learn, feel, and decide. These aren’t token names. These are women rewriting the rules of what machines can learn, feel, and decide. These aren’t token names. These are women rewriting the rules of what machines can learn, feel, and decide. Why This Matters? Why This Matters? The global AI narrative has been shaped for too long by a narrow demographic. When AI is designed without diversity in its creators, it risks reinforcing harmful stereotypes and overlooking marginalized communities. As AI integrates deeper into everything, from healthcare diagnostics to financial lending, it becomes a social responsibility to ensure that its architects reflect the world it’s meant to serve. a social responsibility Yet women still make up only about 22% of AI professionals globally, according to the World Economic Forum. That gap isn’t just about representation-it’s a blind spot in innovation, ethics, and empathy. 22% of AI professionals A Call to Action A Call to Action If you’re in tech: mentor a woman. If you’re hiring: diversify your team. If you’re building products: question your data. If you’re writing AI policy: listen to the voices that have been there since day one but haven’t always had the mic. The future of AI is not just about what machines can do-it’s about who gets to shape that future . who gets to shape that future Let’s make sure it’s a future built by everyone, for everyone. Let’s make sure it’s a future built by everyone, for everyone.
