Artificial intelligence (AI) is no longer just the future; it influences our daily decisions, from what we see on social media to how we access educational or job opportunities. Yet behind every intelligent system are data, algorithms, and human choices that are far from neutral.
The expansion of AI across all sectors, from banking to healthcare, education, justice, and entertainment, forces us to urgently reflect on the risks of bias it carries.
Consider, for example, a loan system that systematically rejects young women because historical data shows they were granted credit less often, or a hiring algorithm that prioritizes men when analyzing resumes because leadership positions have historically been held mostly by them.
These are not problems of the future; they are happening today.
What do we mean by AI bias?
AI bias happens when a system makes decisions that unfairly favor some people over others. For example, a tool might recognize men’s faces better than women’s, or suggest different jobs based on gender.
This occurs because these systems learn from past data. If that data is incomplete, unbalanced, or biased, the technology reproduces the same errors.
If the teams designing these solutions lack diversity or fail to question assumptions, the problem grows. Instead of helping create a fairer world, technology can reinforce existing inequalities.
There are different types of bias in AI:
- Data bias
- Representation bias
- Measurement bias
- Algorithmic bias
A widely discussed case was Amazon’s hiring system, which automatically rejected female candidates because the historical data was overwhelmingly filled with male hires.
Another case is COMPAS, a US criminal justice algorithm that assessed black defendants as more likely to reoffend than white defendants in equivalent situations.
Why gender matters in AI development
Gender bias is one of the most common (and often overlooked) forms of AI bias. Many AI systems are trained on historical data that underrepresents or misrepresents women.
This has real-world consequences: facial recognition works better for men, voice assistants reflect gender stereotypes, and recommendation models reinforce traditional roles.
A surprising example: ask your AI assistant to draw “a person working as a CEO” or “someone performing a heart transplant,” and notice the gender it assigns. Pay attention to how it addresses you; it often defaults to masculine unless instructed otherwise.
Diversity in development teams isn’t just a matter of values but a technical requirement. Algorithmic decisions affect people, and only diverse perspectives can create fairer systems.
Beyond representation in images or text, gender bias also seeps into everyday aspects like digital advertising: a 2018 study showed that Google more frequently displayed higher-paying job ads to users identified as men.
Similarly, the most popular voice assistants were initially designed with female voices and submissive responses, perpetuating the idea that women exist to “serve.” Even in image generators like DALL·E or Stable Diffusion, highly skilled professions are predominantly depicted as male, while caregiving or service roles are shown mostly as female.
Do you want to stay on top of the latest trends in eLearning, EdTech, and Human Resources?
Fill out the form to receive our weekly newsletter with industry insights from our experts.
Intersectional perspective on bias in AI
Talking about gender in AI is crucial, but not sufficient. Bias rarely acts in isolation; it often intersects with other factors such as race, age, sexual orientation, disability, or socioeconomic status. This is what we know as an intersectional perspective.
For example, studies have shown that facial recognition fails more often for black women than for white men, which amplifies inequality. Similarly, some hiring algorithms penalize older candidates, overlooking their work experience. Or consider accessibility technologies that are not designed with people with disabilities in mind, limiting their autonomy.
The intersection of gender with other variables can create even more severe forms of discrimination. A middle-aged migrant woman, for instance, may face multiple barriers in AI systems that, in theory, should be neutral.
This intersectional approach reminds us that AI bias does not affect all women equally; it is distributed unevenly depending on other factors.
The importance of diverse teams
Developing AI is not only a technical task but also an ethical and social one. If teams don’t reflect real-world diversity, the risk of exclusion increases.
As a woman in tech and a mother, I know how easy it is to design systems with a “standard user” in mind, unintentionally leaving many people out.
I once got asked if I’d be comfortable leading a tech team while raising two small children, a question no male colleague had ever faced. That moment made me realize bias is often subtle but present, shaping decisions invisibly. If we don’t address it, it perpetuates.
We must create inclusive teams and recognize that tech decisions have profound impacts. I encourage women, girls, and career changers to explore AI, especially in areas where they can influence system fairness and bias management.
The lack of diversity in AI is evident in the data: according to UNESCO, only 22% of professionals working in the field of artificial intelligence are women. In Latin America, this figure is even lower.
This is not only an injustice in terms of representation but also a loss of talent for the sector. World Bank studies show that diverse teams are more innovative and generate more competitive solutions, something vital in an environment where technology is advancing rapidly.
There are positive examples: companies that have implemented inclusion programs have significantly reduced bias in their algorithms, improving both the accuracy and trustworthiness of their systems.
AI bias is so important that it’s regulated by law in Europe
The EU has created the AI Act to address AI bias, responding to the rapid growth of technologies like ChatGPT and other generative AI, which raise new ethical, legal, and social challenges.
A core principle is preventing AI from making unfair decisions. Companies must implement measures to control bias from the design stage, including using balanced data, auditing results, and documenting decisions.
If AI is going to be part of our daily lives, the minimum we can expect is fairness for everyone.
The EU AI Act classifies AI systems into four risk levels: minimal, limited, high, and prohibited. “High-risk” systems, such as those used in hiring, credit access, or justice, must meet strict transparency and auditing requirements.
For example, they must demonstrate that their training data is representative and that they have undergone bias testing.
Outside Europe, other countries are also making progress: in the US, several states have proposed laws to audit HR algorithms. In Canada, work is underway on the AI and Data Act, and in Latin America, initiatives like the Ibero-American Charter of Ethical Principles for AI have already been launched.
All signs point to regulation being key to preventing digital gaps from becoming structural inequalities.
How can we move forward?
Key actions to address AI bias constructively:
- Evaluate training data to see who is represented and who is excluded.
- Include women and minorities in all stages of AI product development.
- Create spaces for teams to reflect on social and ethical effects.
- Comply with regulatory frameworks like the EU AI Act, promoting safe, transparent systems that respect fundamental rights.
The role of civil society and education
We cannot leave all responsibility in the hands of technology companies. Civil society, universities, and non-governmental organizations play a crucial role in monitoring and improving AI systems.
Citizens can demand greater transparency, ask how algorithmic decisions that affect their lives are made, and support regulations that protect their rights.
At the same time, critical digital education is essential: it is not enough to learn how to use technology; we must understand how it works and what biases it may contain. By teaching new generations to question algorithms, we empower them not to accept unfair decisions as inevitable.
Universities can also integrate AI ethics courses into their curricula, as is already happening in countries like Finland and Canada. This way, we will train professionals who are more aware and prepared to design inclusive technologies.
Technology for everyone
AI can positively transform society, but only if built with empathy, care, and diversity. It’s not just about avoiding technical errors; it’s about imagining a digital future where no one is left out.
Technology is more than code; it’s culture, values, and human decisions. To reflect the real world, all kinds of people need to participate in its creation.
At Smowltech, where we develop AI-based authentication solutions, compliance is not enough. We aim to create products that work well for everyone. A fair system isn’t just ethical; it’s more accurate, reliable, and competitive.
The challenge is in our hands. We can either accept AI that reproduces the mistakes of the past or build AI that helps us overcome them. Every step matters: from demanding transparency from the platforms we use to encouraging more women and underrepresented groups to participate in designing these technologies.
AI is not neutral; it reflects who creates it and for what purpose. That is why the conversation about bias is not an isolated technical debate, but an urgent matter of rights, justice, and our shared future.
Updated on
