DESIGNING AN ETHICAL DATA SCIENCE FRAMEWORK FOR RESPONSIBLE AND TRANSPARENT AI INTEGRATION

Abstract

The growing use of data-centric systems and artificial intelligence (AI) in fields such as business, government, and healthcare has led to an increase in the number of ethical concerns around transparency, privacy, accountability, and environmental impact. This article introduces the Ethical Data Science Framework (EDSF) and formally presents it for publication. It comprehensively outlines ethical principles for the regulation of AI research and development. The EDSF is designed for practical implementation through a hierarchical structure that includes governance mechanisms, technological toolkits, documentation standards, and continuous monitoring processes. The framework is built upon five core pillars: fairness, accountability, transparency, privacy, and sustainability (FATPS). The study provides mathematical definitions of key concepts, measurement protocols, auditing methods, governance roles, artifact templates, and guidance on integrating CI/CD and infrastructure practices. Domain-specific case studies in credit scoring and healthcare diagnostics are presented to demonstrate its strengths and limitations. The appendix includes a governance checklist, pseudocode, evaluation methodologies, practical templates, and mathematical derivations. Keywords: Ethical AI, Fairness, Explainability, Differential Privacy, Governance, Model Cards, Datasheets, Responsible AI, Monitoring, Audit.