INTRODUCTION
In an era where artificial intelligence (AI) is no longer confined to science fiction, but appears in everyday tools such as voice assistants, recruitment algorithms, and e-commerce platforms, we must ask ourselves: who truly benefits from its capabilities?
While AI promises revolutionary innovations, it also carries the risk of amplifying existing social prejudices, including those related to gender and ethnicity. In this article, we explore how AI might develop towards a fairer and more inclusive future.
BIAS: AN ESSENTIAL DEFINITION
Before proceeding further, it is important to define the term “bias”. In the context of artificial intelligence, “bias” refers to unintentional prejudices that can emerge from the data used to train algorithms. These prejudices can cause unfair or discriminatory behaviour by AI-based technologies, often reflecting pre-existing social inequalities.
HUMANS SHAPE THE MACHINE
AI, however advanced, is not immune to human error. It learns from the data we provide, and this data is often steeped in the social inequalities that characterise our history and culture. Gender biases, in particular, are a significant issue. For instance, facial recognition systems demonstrate a 99% accuracy rate in recognising white male faces, but this figure drops drastically when it comes to female faces from ethnic minority backgrounds, with error rates of up to 34% (MIT Media Lab).
This problem extends beyond recognition technology: in recruitment algorithms, if a system is trained on data reflecting a predominance of men in leadership roles, it may develop a bias towards male candidates. It is therefore essential to ask: are we unconsciously allowing our technology to reinforce centuries-old disparities?
We could address this risk by integrating balanced datasets into our algorithms that genuinely represent gender, ethnic, and cultural diversity.
Consider also that in 2021, as today, women accounted for roughly 50% of the global population, yet only 22% of AI professionals were women, compared to 78% who were men. (Deloitte, 2021)
What does this mean?
The low representation of women in technology influences how AI models are trained. Increasing their presence by fostering greater inclusion within AI development teams would therefore help mitigate potential gender-related biases.
The human touch in AI is not limited to the initial data input phase; it is an ongoing process that requires regular, continuous reviews conducted by interdisciplinary teams, including experts in ethics, social sciences, and technology.
These teams are vital for identifying and rectifying unintentional biases that more homogeneous development teams might overlook.
It is within the richness of bringing together diverse perspectives that both ethical soundness and practical effectiveness in AI can be cultivated.
The reason is far from trivial: when systems are tested and developed by a diverse group of people, the resulting solutions are far better suited to a wide range of real-world applications, bridging worlds that may seem entirely distinct at first glance but are bound by a common human factor.
Conclusions
The evolution of artificial intelligence (AI) offers immense opportunities to enhance everyday life, yet it also presents significant ethical challenges. It is vital that AI developers proactively address gender bias and other forms of discrimination.
By adopting an inclusive and responsible approach—grounded in diversity, continuous learning, and rigorous oversight—AI can be guided towards a model of development that benefits all members of society equally. It is equally true that the ability to use this technology wisely, understanding its limits and its potential, lies within human conscience. It is up to us to harness AI responsibly, ensuring its advancements are used to foster equity and justice rather than perpetuate or amplify existing divides.
