Explainable AI (XAI) refers to artificial intelligence systems designed to make their decisions or predictions easier for people to understand. Instead of providing only an outcome, these systems can show the main factors or reasoning that influenced a result. This helps users understand how an AI model reached a conclusion and identify potential errors, unexpected behavior, or bias.
For example, a bank using AI to evaluate loan applications can use XAI to show which factors, such as income, credit history, or existing debt, influenced a recommendation. This makes automated decisions easier to review and question. XAI is useful in finance, healthcare, security, customer service, and other areas where understanding AI decisions is important. It improves transparency, trust, accountability, and human oversight.
