Description: XAI stands for Explainable Artificial Intelligence, which seeks to make the results of AI systems understandable to humans. As artificial intelligence has integrated into various applications across diverse fields, the need for users to understand how and why certain decisions are made has emerged. XAI focuses on developing models that are not only accurate but also transparent, allowing users to interpret the results and trust them. This is especially crucial in sectors where automated decisions can significantly impact people’s lives. The main features of XAI include interpretability, transparency, and the ability to provide coherent explanations about the internal workings of AI models. The relevance of XAI lies in its potential to mitigate bias, increase user trust, and comply with ethical and legal regulations that require clarity in automated decision-making processes.
History: The concept of Explainable Artificial Intelligence (XAI) began to gain attention in the 2010s when it became evident that AI models, especially those based on deep learning, were often ‘black boxes’. In 2016, DARPA (Defense Advanced Research Projects Agency) launched a research program on XAI to develop methods that would make AI systems more understandable. Since then, there has been significant growth in the research and development of XAI techniques, driven by the need for trust and transparency in critical applications.
Uses: XAI is used in various fields, including healthcare, where professionals need to understand the recommendations of diagnostic systems; in finance, to justify credit decisions; and in the legal field, where transparency in automated decisions is required. It is also applied in the automotive industry to explain the decisions of autonomous driving systems.
Examples: An example of XAI is the use of decision tree models, which are easier to interpret than deep neural networks. Another case is the LIME (Local Interpretable Model-agnostic Explanations) system, which provides local explanations for the predictions of complex models. In various industries, tools have been developed that explain the decisions of algorithms, allowing stakeholders to better understand the recommendations provided.
