<![CDATA[The rapid advancement of Artificial Intelligence (AI) and data science technologies has transformed organizational decision-making across healthcare, finance, and digital services. Despite these advancements, the increasing use of personal data has intensified concerns regarding privacy, transparency, ac countability, and public trust. Building trustworthy Human-Centered AI requires data science workflows that integrate regulatory compliance, ethical principles, and responsible governance throughout the AI lifecycle. This study aims to examine how GDPR-compliant data science workflow implementation supports the development of trustworthy Human-Centered AI through a qualitative and practice-oriented research approach. The study synthesizes evidence from recent literature, industry case studies, expert perspectives, and governance oriented analytical frameworks to identify effective strategies for integrating privacy by design, data minimization, transparency, accountability, and privacy-preserving techniques, including anonymization, pseudonymization, and differential privacy, into data science workflows. The findings indicate that successful implementation depends not only on technical safeguards but also on strong organizational governance, continuous compliance monitoring, and cross-functional collaboration among legal, technical, and managerial stakeholders. Furthermore, the integration of explainable and governance-aware machine learning models improves transparency, strengthens stakeholder trust, and supports responsible human-centered AI without significantly reducing analytical performance. This study proposes a structured GDPR-compliant data science workflow framework that enables organizations to balance analytical effectiveness, regulatory compliance, and human-centered principles while fostering trustworthy, transparent, and sustainable Artificial Intelligence for real-world digital innovation.]]>
