Explainable Artificial Intelligence is a set of techniques and approaches that allow us to understand how
AI systems make decisions. It aims to make artificial intelligence "
reasoning" transparent, explaining in a human-comprehensible way why an
algorithm reached a specific conclusion.
Many advanced
AI systems based on techniques such as
neural networks and
deep learning function as "black boxes" where the input data and results are known, but it's not evident how one led to the other. XAI seeks to open these black boxes, allowing users, developers, and regulators to understand the decision-making process.
This transparency is crucial in sensitive applications such as medical diagnoses, autonomous driving systems, credit evaluation, or judicial systems, where decisions directly affect people and where detecting and correcting
biases or errors is important. For example, an XAI loan evaluation system would not only indicate whether an application is approved or rejected but also what specific factors led to that decision.
XAI techniques include visualizations, natural language explanations, and methods that identify which information was most decisive. This ability to explain not only generates trust in the technology but is also increasingly a legal requirement in many jurisdictions that regulate the use of AI.