Black Box

The black box is a metaphor used in machine learning, particularly for deep learning models, referring to the phenomenon where the system's internal decision-making mechanism is opaque and uninterpretable to the human observer. Although the input data and the resulting output are known, the complex, non-linear transformations occurring in the intermediate layers make it impossible to precisely trace why the model arrived at a given conclusion. This property poses a significant challenge in critical application areas (e.g., medical diagnostics, finance) where accountability and explainability (XAI) are fundamental requirements. Reducing the black box nature is a highlighted research direction, serving to increase trust in automated systems.