Description: The theoretical foundations of explainable AI refer to the principles and theories underlying the development and functioning of artificial intelligence systems, with a particular focus on the transparency and interpretability of their decisions. These foundations encompass various disciplines, including computation theory, statistics, information theory, and cognitive psychology. Explainable AI seeks not only to improve the accuracy of AI models but also to make their decision-making processes understandable to users. This is crucial in applications where automated decisions can have significant impacts, such as in various fields including healthcare, law enforcement, finance, and other critical areas. The ability to explain how and why an AI system reached a specific conclusion is essential for building trust and facilitating the adoption of these technologies. Furthermore, explainable AI faces technical and ethical challenges, such as the need to balance model complexity with the clarity of explanations. In summary, the theoretical foundations of explainable AI represent a growing area that aims to make artificial intelligence more accessible and accountable, promoting a deeper understanding of its capabilities and limitations.
