Responsible Data Science: Governance, Risk Management, and AI Compliance in Modern Computing

Abstract

In the present-day world of computing where AI and data-driven systems play an important role in shaping vital decisions in many industries, responsible data science is necessary. This review analyzes the perspectives and governance systems, risk management plans, and mechanisms of compliance that allow ethical, transparent and accountable use of data. The essential issues are examined, namely, bias, privacy issues, and the change of regulations, whereas the tools and techniques used to tackle them involve bias detection, explainable AI, and methods of privacy preservation. Organizations can reduce the risks and enhance innovation through incorporating governance, compliance and high-order monitoring practices. The review offers best practices and the direction to build trustful, fair, and mutually socially responsible AI systems.