Model Explainability in AI refers to the methods and techniques used to understand and interpret the decisions, predictions, or actions made by artificial intelligence models, particularly in complex models like deep learning. It aims to make AI decisions transparent, understandable, and trustworthy for humans.

• Healthcare
• Finance and Banking
• Autonomous vehicles
• Robotics
• Recommendation systems
• Data security
• Computer vision
• Gaming

For instance, an image classification model may use Model Explainability techniques to identify which features of an image contributed most to its prediction, helping users understand how the model arrived at its decision.

• Semantic Data Model – Introduction