Model interpretability refers to the ability to understand and explain how an AI or machine learning model arrives at its decisions.It is crucial for building trust, especially in high-stakes fields like healthcare, finance, and law. Interpretable models help users validate...

Explainable AI refers to a set of techniques and methods designed to make the decision-making processes of machine learning models transparent and understandable to humans. These methods are essential in applications where trust, fairness, and accountability are required, such as...

A White-Box Model is a type of machine learning model whose internal logic is fully transparent, interpretable, and understandable by humans. Unlike black-box models, white-box models allow users to trace exactly how input features influence predictions, making them ideal for...

A Black-Box Model refers to a machine learning or AI model whose internal workings are not easily understandable or interpretable by humans. These models often produce highly accurate results, but it’s difficult to explain how or why they arrived at...

Model Interpretability refers to the degree to which a human can understand the reasoning behind a model’s decisions or predictions. It plays a crucial role in evaluating whether an AI model behaves reliably, fairly, and transparently—especially in high-stakes or regulated...

API (Application Programming Interface) is a set of defined rules and protocols that enable different software applications to communicate with each other. APIs simplify software development by abstracting complex operations and exposing only necessary functionality. They allow systems to interact,...

AIaaS (Artificial Intelligence as a Service) is a cloud-based service model that provides AI tools and infrastructure on demand. It enables businesses to access machine learning models, natural language processing, and other AI capabilities without building or maintaining their own...

Cloud AI refers to the delivery of artificial intelligence services through cloud computing platforms. It enables organizations to access powerful AI capabilities—such as machine learning, natural language processing, and computer vision—without building or maintaining their own infrastructure. As a result,...

Edge AI refers to the deployment of artificial intelligence directly on edge devices such as smartphones, sensors, or IoT hardware—rather than relying on centralized cloud servers. This approach enables real-time data processing, reduces latency, and enhances privacy by keeping data...