Artificial Intelligence (AI) is often perceived to be a neutral, objective tool – just code and computation, free of human flaws. But behind the screen, AI reflects the very systems, structures, and social dynamics that shape our world. Among the most persistent and overlooked of these is gender bias.
When Does Bias Enter AI?
Early and often. And without active intervention, it doesn’t just stay, it compounds.
One of the earliest and most revealing examples of gender bias in AI dates back to 1982, when St. George’s Hospital Medical School in London deployed an admissions algorithm to streamline applicant screening. Originally designed to replicate the human assessors, the model ended up penalising applicants with non-European names and women – because that’s what the historical admissions data showed. The AI simply “learned” from human prejudice and reproduced it at scale.
This story, now well-known in digital ethics circles, reveals a fundamental truth: bias in AI is rarely accidental. It stems from the data we feed into algorithms. Data that itself is a reflection of long-standing gendered, racial, and cultural systemic inequalities.
The bias in AI doesn’t begin at deployment; it begins during design. At every stage of the AI pipeline which includes data collection, labeling, model development, and implementation, there are opportunities for bias to creep in and get ingrained into the system’s logic.
Data is often the first culprit. Giving AI training datasets which underrepresent women, LGBTQIA+ individuals, or people of color, AI learns an incomplete view of the world. This is particularly damaging in sectors like hiring, admissions and medical aid where historical data reflects decades of gender and other discriminations. Labeling adds another layer of bias, as annotators, often outsourced and poorly paid, bring their own assumptions to classification tasks. Even the modeling process can amplify bias, especially if developers don’t test systems across diverse groups.
And once deployed, biased AI can perpetuate harm in subtle but powerful ways: women being shown lower-paying jobs in online ads, virtual assistants programmed to respond submissively to abuse, facial recognition software misidentifying darker-skinned women far more than white men. These aren’t bugs; they’re the consequences of design failures rooted in who gets to build, define and train AI systems.
Reflections of the Sector’s Diversity Deficit
The biases encoded in AI are not just technical but also cultural. They are directly shaped by who is in the room when decisions are made. Today, tech remains one of the least diverse industries, particularly at leadership levels. Most AI development teams are overwhelmingly male, cisgender and from economically privileged backgrounds. Non-binary and trans voices are drastically underrepresented, if acknowledged at all. LGBTQIA+ employees in tech are 20% less likely to be promoted compared to their non-LGBTQIA+ counterparts.
This homogeneity impacts not only the inclusivity of AI systems but also their innovation potential. Diverse teams bring broader perspectives, question assumptions, and foresee harms that more uniform groups might miss. Without them, AI tools risk reinforcing dominant worldviews and excluding those already pushed to the margins.
What Does Bias Look Like in Practice?
AI is not only biased in how it functions but it also transforms the labor market in ways that disproportionately affect women. Roles traditionally held by women, such as administrative support or customer service, are among the first to be automated. Men, by contrast, are more likely to enter AI-augmented roles that offer higher pay and future-proof skills.
Data shows that men are also more likely to apply for these new jobs, engage in upskilling, and express confidence in adapting to an AI-driven economy. As a result, AI risks accelerating existing gender gaps in employment, income, and opportunity—unless proactive measures are taken.
To effectively respond to bias, we must first understand its forms. Broadly, there are two types of bias:
- Explicit Bias: Intentional prejudices of an individual
- Implicit Bias: Unconscious biases stemming from societal conditioning
These two biases typically realise in different ways during the AI application. Some of the common use cases which are seen are:
- Selection Bias arises when datasets omit or underrepresent certain gender identities.
- Stereotyping Bias reinforces gender roles—associating women with caregiving or men with leadership, for instance.
- Measurement Bias distorts outcomes when data about marginalized genders is sparse or poorly defined.
- Confirmation Bias causes AI to replicate the preferences of past decision-makers—often reinforcing sexist trends.
- Out-Group Homogeneity Bias lumps diverse identities into oversimplified categories, erasing nuance in favor of efficiency.
Each of these biases is dangerous on its own. Together, they create systems that seem neutral but are deeply exclusionary.
AI for Inclusion?
Despite these challenges, AI also holds promise as a force for equity if developed intentionally. With the right data, teams, and governance, AI can help identify hidden inequalities, make decision-making more accountable, and build systems that serve everyone.
Efforts are already underway. Organizations are conducting bias audits, using intersectional data collection, and creating inclusive design protocols that center marginalized voices. Increasing the representation of women, non-binary, and trans professionals in AI development is not just an ethical priority—it’s a design imperative. Their presence helps challenge norms, question biases, and reimagine AI’s potential.
Governments, too, have a role to play: by enforcing transparency standards, protecting digital rights, and incentivizing ethical AI development. Civil society and feminist tech collectives are also stepping in—creating counter-narratives and community-led models of technological innovation.
To build AI that is truly intelligent and just, we must be willing to question not only our algorithms but our assumptions. With diverse teams, inclusive data, and intentional governance, AI can become not just smarter, but fairer.
Sources:
- Generative AI and Gender: Global Employment Trends : https://www.linkedin.com/posts/draskod_generative-ai-gender-global-employment-activity-7315867961976610816-BMRc/?utm_source=share&utm_medium=member_android&rcm=ACoAABGuxhsBvjM6TqhG3BpARkgSAYZB-zeZ2eY
- Bias in AI – Introduction : https://www.chapman.edu/ai/bias-in-ai.aspx#:~:text=Data%20Collection%3A%20Bias%20often%20originates,lead%20to%20biased%20hiring%20recommendations.
- The origins of bias and how AI may be the answer to ending its reign : https://medium.com/design-ibm/the-origins-of-bias-and-how-ai-might-be-our-answer-to-ending-it-acc3610d6354
- Untold History of AI: Algorithmic Bias Was Born in the 1980s : https://spectrum.ieee.org/untold-history-of-ai-the-birth-of-machine-bias
- How AI reinforces gender bias—and what we can do about it : https://www.unwomen.org/en/news-stories/interview/2025/02/how-ai-reinforces-gender-bias-and-what-we-can-do-about-it
