Algorithmic bias: even artificial intelligence stumbles over prejudice

Everyone, at least once in their life, has fallen into the trap of prejudice: an opinion formed in advance, often based on characteristics such as ethnicity, gender, religion, sexual orientation, or age. Artificial intelligences, powerful and increasingly widespread tools, are not exempt from these prejudices—also known as biases—that afflict human society. On the contrary, they often amplify and perpetuate them.

Artificial intelligence and prejudice

A telling example is that of recruitment companies, where artificial intelligence (AI) is used to identify potential candidates. If, for instance, a company is looking for a manager, these algorithms tend to systematically discriminate against certain categories of people. Women, migrants, and young people, for example, are incorrectly associated with lower-level job positions. This happens because the algorithms have been trained on historical data that reflect existing social inequalities, where, in this case, the role of manager had always been held by adult Caucasian men.

Machines learn from us

The heart of the problem lies in the way AI systems are trained. Through machine learningIl termine apprendimento - con i sinonimi imparare, assimila... More, algorithms learn from the data they are given, which are often the result of human activity—and therefore carriers of our prejudices. Whether it concerns ethnicity, gender, sexual orientation, or geographic origin, our preconceptions seep into models and influence the decisions made by machines.

Not just a simple programming error

Those who create algorithms are human intelligences. And it is precisely human intelligence that is the first not to be immune to social prejudice. Often, without realizing it, developers introduce their own personal beliefs into their creations, even when these are unconscious.

Algorithmic bias, therefore, is not merely a programming error, but something far more complex. Like the biases of human intelligence, if they are not identified and mitigated, they infiltrate machine learning processes, perpetuating inequalities and discrimination, effectively reinforcing all our prejudices.

 

 

The first step toward fairer AI

To mitigate these risks, a multidisciplinary approach is needed—one that involves technicians, ethics experts, and representatives of civil society, but also and above all an empathetic approach on the part of developers.

Eliminating algorithmic bias is the first step toward achieving more equitable and ethical artificial intelligence. But how can prejudices be eliminated if they are a characteristic of the human mind? They cannot be eliminated entirely, but they can be reduced, starting from the ground up—for example, through school programs that foster the development of emotional intelligence in each individual.

Fondazione Patrizio Paoletti, together with New Life For Children, has implemented the Teachers Outreach project, a global platform dedicated to the professional development of educators and teachers around the world, based on Pedagogy for the Third Millennium. The goal is to train teachers who can convey relational and emotional skills in the classroom—skills that help reduce prejudice toward others. This project supports the growth of emotionally intelligent generations and a global mindset that encourages students to view their experiences and knowledge from a broader perspective, fostering empathyEmpathy is a fundamental skill that allows us to connect wit... More and understanding toward different cultures and realities. A more conscious global mindset and a more equitable and ethical artificial intelligence both contribute to the global well-being of individuals and society, which depends on overcoming bias and prejudice.