In recent years, the conversation about artificial intelligence and employment has been dominated by a substitution narrative: Which jobs will disappear? How many jobs will be replaced by algorithms? While this question is important, it has led us to view the future of work from a narrow perspective…

How should we understand the concept of developing Artificial Intelligence for the common good? This is a key question, which, according to philosopher Diana Acosta Navas, opens up two central dimensions: one philosophical and the other political…

In recent years, language models have transformed the way we interact with information. From virtual assistants to decision-support systems, these tools have become omnipresent…

In recent years, artificial intelligence models have demonstrated incredible potential to transform industries, from healthcare to finance. However, they have also exposed a troubling issue: algorithmic bias.

Machine learning models have become essential tools for decision-making in critical sectors such as healthcare, public policy, and finance. However, their practical application faces two major challenges: selection bias in the data and the proper quantification of uncertainty.

This blog is an extended summary of the article Riascos, A. (2025). Since the beginning of the so-called third wave of neural networks (Goodfellow et al., (2016)) in the first decade of this century, there has been great hope in the possibilities of artificial intelligence to transform all human activities. At the same time, warnings have been raised about the risks involved in the introduction of this new technology (Bengio et al., (2024)).