Ethical Implications of AI in Decision-Making

Artificial Intelligence (AI) is increasingly integrated into decision-making across healthcare, finance, criminal justice, and public policy. While promising scale and efficiency, AI systems pose concrete ethical risks including bias fragility under shift, opaque decision boundaries, explanation fidelity gaps, and privacy–utility tradeoffs. This paper synthesizes recent IEEE standards and technical studies to propose a reproducible evaluation protocol, lifecycle artifacts, and CI/CD gating criteria for ethical AI deployment. We outline governance steps, monitoring practices, and stakeholder integration strategies to operationalize fairness, accountability, transparency, and privacy in high-stakes domains.