Description: Antecedent Analysis in the context of Explainable Artificial Intelligence (XAI) refers to a systematic method for examining previous data or events that may influence decision-making within AI systems. This approach seeks to provide a clearer understanding of how and why an AI model reaches certain conclusions, which is fundamental for increasing user trust and acceptance of these technologies. By analyzing the antecedents, patterns, biases, and factors affecting model performance can be identified, allowing developers to adjust and improve their algorithms. Transparency in decision-making processes is essential, especially in critical applications such as medicine, justice, and finance, where automated decisions can significantly impact people’s lives. In this sense, antecedent analysis focuses not only on the data used to train models but also on the context in which they are applied, ensuring that decisions are fair and ethical. This approach has become increasingly relevant in a world where AI is integrated into various facets of daily life, and where the need for clear and understandable explanations becomes imperative for human-machine interaction.
