I have been in education for over thirty years. I have sat in laboratories, stood at the front of lecture halls, designed training programmes, and mentored students across every stage of learning. In all that time, one pattern has remained stubbornly consistent: the closer you get to the top of science and technology fields, the fewer women you find.
That is not an accident. It is the result of a long chain of small decisions, quiet discouragements, and structural gaps that, taken individually, seem manageable. But together they add up to something significant.
I want to talk about that. Not in the abstract, and not with the kind of polished language that fills conference programmes and leaves nothing changed. I want to talk about what I have actually seen, and what I believe we need to do about it here in Jamaica and across the Caribbean.
The Problem Starts Early
By the time a girl reaches fourth form and has to choose her CSEC subjects, the decision has often already been made for her. Not by a single person, but by years of accumulated signals. Which subjects her family assumed she would be good at. Which teachers invested more energy in the boys who asked louder questions. Which careers were described as "for people who are good with numbers" in ways that somehow never seemed to include her.
I have watched this happen. Bright, curious girls who asked excellent questions in primary school, who showed real analytical instinct, who could dissect an argument with precision, slowly learning to be smaller in classrooms that rewarded a certain kind of confidence. By the time university arrives, the drop-off is already baked in.
The Caribbean is not unique in this. But we do have specific conditions that sharpen the problem. In many of our communities, the financial pressure on families means girls are often steered toward what looks like a safe, practical path. Teaching, nursing, administration. These are valuable, important careers. But they should be chosen freely, not defaulted into because no one held open the door to anything else.
The Cost of a Missing Perspective
When I started working in the field of AI and data science, something struck me almost immediately. The tools being built, the models being trained, the systems being deployed: they reflected the perspective of the people building them. And those people were, overwhelmingly, men from a small number of countries and backgrounds.
This is not a small thing. AI systems learn patterns from data. If the data reflects a world where women are underrepresented, and if the teams designing those systems do not include people who will notice that gap and push back on it, then the output will encode and amplify existing inequalities.
We have already seen this. Facial recognition systems that perform significantly worse on darker skin tones and on women. Hiring algorithms that penalise resume gaps, which disproportionately affect women who took time for caregiving. Medical research datasets where women are chronically underrepresented, leading to diagnostic tools that are less accurate for them.
These are not theoretical risks. They are things that have already happened, and that are already affecting real people. The solution is not just better data, though we need that. The solution is more diverse teams who bring different questions to the design process before the damage is done.
When Caribbean women build AI systems, they bring a perspective that Caribbean communities need and deserve. They know what the water access problems look like in rural St. Mary. They understand how informal economies work and why a credit scoring model built for a salaried workforce will fail for a market vendor in Coronation Market. They carry knowledge that no dataset fully captures.
That knowledge belongs in the room where the technology is designed. Keeping it out is not just unfair to the women being excluded. It is a practical mistake that makes the technology worse.
What Actually Works
I want to be direct here, because I have sat through enough panels and read enough reports to know that good intentions and beautiful language do not change enrolment numbers.
What works is access. Genuine, early, repeated access to science and technology in settings where girls are expected to succeed rather than struggle through. Not special programmes designed to fix something broken in them, but learning environments that take their intelligence as a given and build from there.
What works is seeing yourself in the field. I think about the students I teach today, and I know with certainty that representation matters. When a girl sees a Caribbean woman who looks like her working in AI, standing at the front of a data science class, publishing research, building a company, something shifts in what she understands to be possible. This is not sentiment. It is how human motivation actually functions. We extend our ambitions toward what we can see.
What works is removing the financial barrier. Across the Caribbean, the cost of education and training is real. Programmes that are free, genuinely free, do not undercount what this means for the communities they serve. It is the difference between a girl's ambition staying theoretical and it becoming a career path she can actually walk.
What works is having adults around her who treat her curiosity as an asset rather than an inconvenience. This is something I think about constantly in my own work. The students who thrive are almost always the ones who were, at some point, told clearly that their questions were worth asking and their mind was worth developing.
What We Are Doing at The Genius Project
I joined The Genius Project because I believe what it is building is serious. Not a one-time camp, not a feel-good exercise, but sustained, quality programming designed to give young Jamaicans a real foothold in the technology sector.
Our Learning Support Hub exists in part because I know from thirty years of education that the standard classroom design was not built with everyone's brain in mind. Girls with ADHD, dyslexia, or processing differences were already fighting a harder battle in a system that penalised how they learned. Adding AI tools that adapt to different learning styles is not charity. It is the correction of a long-standing design flaw.
We are also deliberate about who we recruit and who we feature. Our Genius Bootcamp cohorts reflect the diversity of Jamaica. Our mentors include Caribbean women working in data, engineering, and machine learning. The message we send, in every cohort, is that this field is for them. Not conditionally. Not with caveats. Without qualification.
There is more to do. We want to build a pipeline that starts younger, that reaches girls in St. Thomas and Hanover and Portland, not just Kingston. We want to track where our graduates go and build the kind of longitudinal data that lets us see what actually works over time. We want to fund more of this, because nothing good is free to run even when it is free to attend.
A Note to the Girls Reading This
If you are a young woman in Jamaica reading this and wondering whether science and technology is for you, I want to say something directly.
Your questions are not too much. Your curiosity is not a problem to be managed. The fact that you are asking whether you belong in these fields tells me you already have the most important quality a scientist needs, which is the drive to understand how things work.
The field needs your perspective. Not just because of equity, though equity matters. But because the problems we are trying to solve with technology are real problems that affect real communities, and the people with the deepest understanding of those communities need to be in the room where solutions are built.
Come in. There is work to do, and you are equipped for it.
A Note to Everyone Else
Representation does not fix itself. It requires people in positions of influence, in schools, in companies, in government, in families, to make different choices about who they invest in, whose potential they take seriously, and what they describe as possible when a young woman is listening.
That is not a burden. It is an opportunity. The talent is there. It always has been. We just need to stop getting in its way.
