The "Blackbox" Problem in AI - Investigating The Transparency of Deep Learning Algorithms In High-Stakes Decision Making (Like Health And Law)

Artificial intelligence has progressed rapidly, but many deep learning systems are still difficult to interpret, leading to the "black box" problem in high-stakes areas such as healthcare and criminal justice. These models can achieve strong prediction performance but their layered, non-linear structure often makes transparency, trust and accountability difficult. The paper examines the conceptual basis of black-box behavior in deep learning and its implications, through the case studies of IBM Watson and COMPAS. It also reviews explainable AI methods like LIME, SHAP as partial solutions for improving model interpretability. Effective deployment of AI in critical decision making requires not only accuracy but also explainability, fairness and human oversight, the study argues.