Ethical and Responsible AI Frameworks for Intelligent Data-Driven Platforms

Abstract

The rapid integration of Artificial Intelligence (AI) into intelligent data-driven platforms has created transformative opportunities across sectors including healthcare, finance, e-governance, and autonomous systems. However, these advancements also introduce ethical tensions, particularly related to privacy, transparency, algorithmic bias, and accountability. This paper explores the design and implementation of Ethical and Responsible AI frameworks tailored for intelligent data platforms. It presents core principles, governance models, risk assessment procedures, and operational guidelines that organizations can employ to ensure trustworthy AI. The study further discusses the multidimensional challenges that arise in practical deployment and offers a conceptual model that aligns regulatory compliance with socio-technical requirements. Two visual charts illustrate trends in ethical compliance and the distribution of risk categories in AI lifecycle management.