The notion refers to the ability to explain an AI model, its expected impact, and potential biases. It contributes to the definition of model accuracy, fairness, transparency, and outcomes in AI-powered decision making. Explainable AI is critical for an organization to build trust and confidence when deploying AI models. As AI advances, humans are challenged to understand and retrace how the algorithm arrived at a result. The entire calculation process is transformed into a ‘black box’, which is impossible to interpret. These black-box models are built entirely from data. Even the engineers or data scientists who created the algorithm are unable to understand or explain what is going on inside them or how the AI algorithm arrived at a specific result. There are numerous advantages to understanding how an AI-enabled system produced a specific result. Explainability can assist developers in ensuring that the system is performing as expected, it may be required to meet regulatory standards, or it may be necessary to allow those affected by a decision to challenge or change the outcome.