<![CDATA[The deployment of artificial intelligence (AI) in safety-critical domains has made ethics and safety priorities. This paper examines AI ethics and safety by synthesizing technical, philosophical, and governance perspectives. It analyzes ethical principles, including fairness, transparency, privacy, accountability, and safety, while examining tensions in their implementation. Technical approaches, including robustness mechanisms, adversarial training, and interpretability methods, are considered alongside frameworks for value alignment and corrigibility. The discussion highlights the challenges of translating values into operational requirements, when competing objectives and contextual differences complicate implementation. The paper also assesses the governance landscape, comparing regulatory approaches across jurisdictions and evaluating binding and non-binding instruments for AI development. It argues that governance requires integrating technical safeguards, ethical reflection, and institutional accountability throughout the AI lifecycle. Moving beyond principle-based declarations, the paper emphasizes auditable and enforceable practices that connect ethical commitments with implementation and oversight. This perspective supports responsible innovation while addressing the risks associated with safety-critical AI deployment.]]>
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