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Ethical AI in Practice: Governance Strategies for Responsible and Accountable AI Deployment examines the widening gap between rapid AI adoption and the slower development of governance mechanisms across institutions. As artificial intelligence becomes embedded in decision-making processes across healthcare, finance, employment, and public administration, the paper argues that the core challenge is no longer technological capability, but the ability of institutions to govern these systems effectively.
The paper analyzes key risks—including bias, opacity, uneven institutional capacity, and operational failures—demonstrating how governance gaps translate into real-world consequences. Drawing on emerging research and comparative policy approaches, it highlights the limitations of fragmented oversight and emphasizes the need for structured, lifecycle-based governance models that integrate risk classification, data governance, validation, human oversight, and continuous monitoring.
Moving from diagnosis to implementation, the paper proposes a practical governance framework supported by a readiness assessment tool to help institutions operationalize ethical principles at scale. It concludes with targeted policy recommendations, calling for coordinated action to embed governance into system design, strengthen institutional capacity, and align oversight with the pace of technological advancement.
