Black Box AI refers to an artificial intelligence system whose internal reasoning or decision process is difficult for people to understand, even when its inputs and outputs are observable. The term is often applied to complex machine learning models, particularly deep neural networks, where millions or billions of learned parameters interact to produce a prediction without providing a straightforward explanation of how each factor influenced the result.
Black Box AI can be useful for tasks such as image recognition, language processing, and fraud detection, where predictive performance may be more important than transparent reasoning. However, limited interpretability can make it harder to identify errors, investigate bias, or justify high-impact decisions. Explainable AI techniques are often used to make these systems more interpretable.
