This study examines how ethical standards established by stakeholders such as developers and policymakers provide top-down guidance aligned with deontological ethics or utilitarian goals. It also highlights a complementary bottom-up approach, rooted in virtue ethics, in which individuals engage in ethical deliberations shaped by their moral values. Both approaches have limitations, and, at times, ethical standards can clash with moral values, thus blurring lines of responsibilities. Deontological principles may offer a structured framework, but often lack adaptability to diverse cultural contexts; bottom-up approaches foster intrinsic moral intentions, but universal applicability may be challenging, thus raising moral dilemmas. Through a theoretical literature review, this study explains how different ontological and normative ethical perceptions lead to moral dilemmas in various AI application scenarios (e.g., algorithmically managed platforms, crime detection systems, medical AI assistants). It addresses top-down and bottom-up approaches that may help account for moral dilemmas ethically. The study discusses the balance between top-down regulatory frameworks and bottom-up community-driven ethics to navigate the complex ethical landscape of AI applications, whose increasing capabilities alter expectations of AI’s agency and morality. This study calls for holistic and multi-objective ethical frameworks that incorporate diverse normative ethical perspectives and recognizes context-specific ontologies throughout the AI lifecycle. It emphasizes a nuanced and context-specific combination of top-down standards (e.g., regulatory oversight, clear guidelines) and bottom-up fostering of moral values (e.g., by improving ethical knowledge). This tailored and ongoing reflection of ethical standards and moral values accounts for an ethical development, deployment, and utilization of AI technologies.