In a world still learning to listen, how do we make sure every voice, including women’s, is heard?
For centuries, women’s voices have been softened, minimized, and sometimes erased. Now, as we train machines to speak for us, we’re faced with a new question: will artificial intelligence learn to mirror those same imbalances?
Finding My Voice.
I’ll admit, I was hesitant to write this article. The very act of writing about women in AI felt tokenizing, like another performance in a world that still struggles to listen.
But silence has never served us.
So I decided to use my voice, not to add to the noise, but to help others hear what it sounds like to exist inside an industry that was not built for you. To share what it means to sit in a leadership room where decisions are made, models are trained, and the future is being written, mostly by male hands.
When I speak about gender in AI, I’m not just talking about representation. I’m talking about authorship. About who gets to define intelligence, empathy, and truth in the languages our machines will learn to speak.
Bias in AI isn’t a technical glitch; it’s a mirror. Every dataset reflects the voices that were loudest, and the ones that were never recorded. Every “neutral” system carries the accents of its creators, the assumptions of its trainers, the stories of those deemed worthy of being included.
When AI speaks, it is reflecting us. Our history. Our values.
And that’s what makes this moment so critical. We aren’t just building systems that respond to human voices; we’re deciding whose voices define what human sounds like.
Training AI is a lot like raising a child. It learns what we feed it, tone, language, and behavior. If we want fairness, we have to raise it with care, balance, and intention. That means stripping out patriarchy, racism, religious bias, and every unspoken assumption baked into the data we’ve been building upon for centuries.
The goal isn’t neutrality; it’s awareness. A child raised to listen before it speaks.
I don’t have all the answers on how to fix the problem. In fact, I don’t have many. But what I have is a seat at the table, and a responsibility to use it.
I’m a woman in leadership trying, day by day, minute by minute, to make AI less biased, more intuitive, and more usable for the people who need it most. Some days it feels like progress; other days, like whispering into a storm.
That conviction shaped the work I lead today with DZ One. DZ One is an AI-powered demand gen co-worker built exclusively with marketers in mind. It’s not a traditional software platform, but an agentic system designed to handle the complexity of multi-vendor lead generation, clean data automatically, and make creating campaigns and pulling subsequent reporting as easy as typing out a message.
For me, DZ One became a way to turn philosophy into action — to build an AI system where empathy, accuracy, and human balance aren’t add-ons, but foundations. I wanted to prove that women’s voices could not only critique bias in AI, but actively code better systems into being.
But I’ve learned that progress is not easy. Sometimes it begins as a question in a meeting no one expected.
“Who does this serve?”
“Whose data is missing?”
“What if the system is wrong?”
Those questions are small acts of defiance, moments when a voice, once dismissed, becomes the mirror that changes the conversation.
There’s data to back it up: when women lead in AI, outcomes improve. Products become more inclusive. Teams see around more corners.
When women, people of color, and marginalized creators are part of the design, something remarkable happens: the tone of intelligence changes. AI starts to sound more human, not because it mimics empathy, but because empathy is finally present in its making.
AI is teaching us as much about ourselves as we are teaching it. It mirrors our brilliance and our bias, our creativity and our carelessness.
The question is not whether AI will replace human voices, it’s whether we’ll like the sound of our reflection.
That’s why I tell my team, my peers, and myself: Stay open. Question everything. Stay curious and observant, especially when dealing with AI.
Because bias doesn’t shout. It whispers. It hides in tone, in defaults, in the quiet moments when no one is listening.
In the end, it’s not about giving AI a voice. It’s about making sure it listens to all of ours.
