The Gender Gap in Technology
Ximena starts a computer engineering program and, on the first day, counts the people in her classroom: out of 40 students, only 8 are women. Is programming easier or harder for women? Or is there another explanation? On this page you will measure that difference, understand where it comes from, and see how it can sneak into even an artificial intelligence.
What the gender gap is
A gap is a difference between two groups that is not explained by their abilities, but by the conditions in which each group operates.
Notice the key idea: a vocation is not born in a vacuum. From childhood, some kids are given building sets and video games, and others are given dolls or kitchen toys. People say that "math is for men." Over the years, those signals weigh on what each person imagines being capable of studying.
Measuring the gap with percentages
To talk with data we use the percentage: what part of the total a group represents.
A useful reference: if participation were equal, we would expect close to $50\,\%$. The distance between that value and the real one measures the gap: in the example, $50 - 23 = 27$ percentage points.
In a computer engineering degree there are 120 students and only 30 are women. Calculate the percentage of women and explain why the low participation is not explained…
In a computer engineering degree there are 120 students and only 30 are women. Calculate the percentage of women and explain why the low participation is not explained by ability, but by factors such as stereotypes, the lack of role models or the atmosphere inside the degree.
Barriers that sustain the gap
The gap has causes that can be named. They are social, not biological:
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Stereotypes: ideas like "technology is a guy thing" that are repeated at home, at school, and in the media.
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Lack of role models: if you never see a woman engineer, a robotics PhD, or a cybersecurity expert, it is harder to picture yourself there.
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Environment in the program: classrooms where women are few, comments that doubt their ability, teamwork where they are assigned "secondary" tasks.
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Online violence: harassment and digital attacks that discourage participation in programming communities, video games, or social media. The UN (2023), cited in the Digital Culture curriculum, identifies it as a major barrier.
Gender bias in algorithms
The gap is not only in classrooms: it can pass on to machines. An artificial intelligence system learns from data. If the data reflect an unequal past, the model learns that inequality and repeats it.
Résumé filtering with AI shows why diversity on teams matters: whoever designs a system decides which data to use and which problems to detect. If the team has only one kind of perspective, it is easier for no one to notice the bias.
A company uses artificial intelligence to screen résumés. Because the model was trained on past hiring where men predominate, it starts rejecting equally capable women's…
A company uses artificial intelligence to screen résumés. Because the model was trained on past hiring where men predominate, it starts rejecting equally capable women's profiles. Explain how gender bias enters the system through the data and propose two measures to avoid it, for example diverse teams developing it or audits of the training data.
What can be done
Closing the gap requires action at several levels:
| Level | Measure | What it addresses |
|---|---|---|
| Family and school | Encourage robotics, programming, and calculus without distinguishing by gender | Stereotypes |
| Visibility | Invite women engineers and scientists to science fairs and high school clubs | Lack of role models |
| Program and company | Mentorships, anti-harassment protocols, schedules and support that make it possible to stay | Environment and dropout |
| Technology | Diverse teams, data audits, and human review in AI systems | Algorithmic bias |
| Digital environments | Moderation and reporting of online violence | Digital harassment |