The term "Explainable AI" (XAI) was first coined by DARPA (Defense Advanced Research Projects Agency) in 2016, as part of their effort to improve human trust in machine learning systems. The goal of XAI is to make AI systems more transparent and interpretable by humans, allowing them to understand how the system makes decisions or predictions.

In traditional machine learning approaches such as deep learning, the models are often considered to be "black boxes" that cannot be easily understood by humans. This lack of interpretability can be problematic in sensitive domains such as healthcare, finance, and criminal justice, where decisions made by AI systems can have a significant impact on human lives. XAI aims to address this issue by developing AI models that can explain their reasoning in a way that humans can understand.

XAI techniques can take many forms, including visualizations, natural language explanations, and interactive interfaces that allow users to explore the decision-making process of the AI system. By providing humans with a better understanding of how AI systems arrive at their decisions, XAI can improve trust in these systems and facilitate their adoption in real-world applications.

In recent years, XAI has gained significant attention from both academia and industry, with many researchers working on developing new methods for making AI systems more transparent and interpretable. The development of XAI is particularly important as AI continues to be integrated into various aspects of our daily lives, and as the consequences of AI decision-making become more significant.