Explainable AI (XAI) pertains to artificial intelligence systems that make decision-making transparent and easier to understand. It allows you to understand and trust machine learning algorithms’ results.
Unlike traditional AI models, which frequently operate as “black boxes” (where the logic behind decisions is hidden), XAI sheds light on “how” and “why” specific decisions are made. This transparency is critical for maintaining trust, particularly if you operate in fields such as healthcare, banking, and law, where the impact of AI choices can be serious.
Explainable AI typically involves using models that are either inherently interpretable, like decision trees, or employing techniques that can explain the outputs of more complex models, such as deep neural networks.
The goal is to make AI choices more accessible to non-experts so that decisions can be scrutinized, validated, and trusted.
