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

The rapid adoption of data-centric systems and artificial intelligence (AI) in industries such as healthcare, government, and business has increased the number of ethical concerns related to transparency, privacy, accountability, and the environment. This research marks the first publication of the Ethical Data Science Framework (EDSF). It establishes moral guidelines for overseeing AI development in its entirety. The execution of the EDSF is facilitated by a hierarchical architecture that incorporates administration, technical toolkits, documentation standards, and continuous monitoring. The five pillars of this system are FATPS: Transparency, Fairness, Accountability, and Privacy. Aside from outlining an implementation strategy that incorporates infrastructure and CI/CD approaches, we also cover mathematical terminology, algorithms, measurement protocols, audit procedures, governance responsibilities, and artifact templates. To demonstrate its utility and drawbacks, we use two domain-specific case studies: healthcare diagnostics and credit scores. An evaluation approach, a governance checklist, mathematical derivations, pseudocode, and practically relevant templates are all part of the study's appendix.