What do we call AI systems designed to provide clear, understandable explanations for their predictions?

AI systems that are designed to offer clear and understandable explanations for their predictions are referred to as explainable AI (XAI). This concept is crucial in the field of artificial intelligence because it addresses the often-cited problem of the "black box" nature of many AI models, particularly those using complex algorithms, such as deep learning.

Explainable AI focuses on creating models that not only perform well but also provide insights into their decision-making processes. This transparency is vital for fostering trust and allowing users to understand the rationale behind AI outputs, which can be essential, especially in sensitive domains like healthcare, finance, and legal applications. By enabling stakeholders to grasp how and why decisions are made, XAI enhances accountability and can facilitate compliance with regulatory standards, making it a fundamental aspect of responsible AI deployment.

In contrast, other options like automated reasoning, predictive analytics, and transparent intelligence do not specifically address the explanations accompanying AI predictions in the same context as explainable AI. They relate to different aspects or functionalities of AI systems but do not encapsulate the crucial element of explanation that XAI embodies.