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"Ethical, Explainable AI: Analysis," explores the critical need for responsible AI frameworks as artificial intelligence becomes more integrated into society. It emphasizes Fairness, Accountability, and Transparency (FAT) as core principles for trustworthy AI, aiming to mitigate issues like algorithmic bias and the "black box" problem of opaque models. The document outlines various explainable AI (XAI) methodologies, including post-hoc techniques like LIME and SHAP, and the benefits of interpretable-by-design models. Furthermore, it analyzes the sources and types of algorithmic bias, suggesting different mitigation strategies across the AI lifecycle, and examines the risks and harms of AI in high-stakes domains such as healthcare and criminal justice. Finally, the text surveys the global regulatory landscape with a focus on the EU AI Act and the NIST AI Risk Management Framework, concluding with strategic recommendations for internal AI governance and a discussion of the long-term challenge of AI alignment with human values.