Artificial Intelligence (AI) has increasingly been integrated into high-stakes domains such as healthcare, finance, and autonomous systems. While these integrations promise significant advancements, they also present substantial ethical challenges, particularly with black box AI models whose decision-making processes are opaque. This paper explores the ethical implications of black box AI in high-stakes applications, focusing on issues such as bias, fairness, accountability, and transparency. Through an in-depth analysis of several case studies—including IBM Watson for Oncology, algorithmic bias in financial lending, and incidents involving autonomous vehicles—we highlight the consequences of opaque AI systems and review current regulatory frameworks. Additionally, we propose comprehensive strategies for addressing ethical concerns, emphasizing the need for enhanced transparency, fairness, and accountability. Our findings suggest that while regulatory and technical advancements are underway, there is a critical need for continued efforts to ensure ethical deployment of AI in high-stakes settings.