What is bias in artificial intelligence?

Bias in artificial intelligence (AI), also called algorithmic or machine learning bias, occurs when the results generated by an AI system reflect biases present in the training data or in the algorithm's design. This phenomenon can have harmful consequences, especially when it disproportionately impacts certain social groups.

Cases such as errors in systems of facial recognition that misidentify racialized people, voice assistants that transcribe speakers with certain accents worse, or personnel selection algorithms that favor men over women exemplify how these biases manifest themselves in practice.

Bias can originate from a lack of representativeness in the data used to train the model, leading to a reproduction of historical social inequalities. It can also arise directly from the algorithms themselves, which, not being designed with equity criteria, end up reinforcing existing discrimination, whether based on gender, race, origin, or socioeconomic status.

To mitigate these risks, it is crucial to use diverse and representative data, avoiding datasets that exclude certain groups. Furthermore, transparency in the systems and the ability to understand how they make decisions—through so-called "explainable AI"—allow for the detection and correction of potential deviations. Finally, it is essential to establish mechanisms for continuous evaluation, regularly auditing the models to ensure they continue to function fairly over time.

Addressing bias in AI is not only a technical issue, but also an ethical and social one. It implies a commitment to developing more inclusive technologies that contribute to a more equitable society.