Explainable AI (XAI) refers to the methods and techniques used in the application of artificial intelligence technology that yield results which can be easily understood by humans. The goal of explainable AI is to create a system that can clearly elucidate its functionality and decision-making to the end user, thereby minimizing the so-called “black box” effect that can often come with complex machine learning algorithms.
Explainability in AI is an essential factor because as AI is becoming more and more integrated into our daily lives, it is crucial that its outcomes and decision-making processes are transparent and comprehensible. This is especially vital in scenarios where AI is used to make significant decisions that impact humans, such as healthcare diagnostics, credit approvals, hiring functionalities, and more.
Explainable AI enables humans to comprehend and trust the actions of AI systems better. When users understand how AI models arrive at their conclusions, they are more likely to trust these systems and their recommendations. It allows for the identification and correction of errors and biases within the system. Explainable AI hence promotes transparency, trust, and a more effective collaboration between artificial intelligence and human users.
