Integrating the Pillars of Ethical AI: A Framework for Managing Fairness, Accuracy, and Interpretability Trade-offs

The development of Ethical AI systems is fundamentally challenged by the need to balance competing objectives: fairness, accuracy, and interpretability. Prior work has treated these pillars in isolation, neglecting their frequent conflicts. This paper directly addresses this trilemma by proposing a novel, integrative framework for managing trade-offs. Our solution provides a structured, four-phase methodology for contextual scoping, technical strategy selection, multi-dimensional evaluation, and governance documentation. A significant finding is that explicit trade-off management, visualized via Pareto frontiers, enables more transparent and justified AI system design, moving beyond simplistic single-metric optimization. We validate the framework's utility through illustrative case studies in healthcare diagnostics and automated recruitment, demonstrating its role as a critical decision-support tool for practitioners and a cornerstone for robust AI governance.