From Data to Wisdom: Human-Centered AI for Ethical and Transparent Decision-Making

Artificial Intelligence (AI) plays a crucial role in systematically transforming raw data into decisions that are meaningful, actionable, and ethically sound, in accordance with the Data–Information–Knowledge–Wisdom (DIKW) hierarchy. This paper explores how AI technologies correspond to each stage of the DIKW framework, facilitating the transition from raw data to wisdom-oriented governance. The discussion covers data collection and preprocessing, information extraction via machine learning, semantic reasoning for knowledge representation, and the incorporation of ethical frameworks to achieve wisdom-driven outcomes. Special attention is given to explainable AI (XAI), fairness, and accountability, which are essential for responsible AI design. Real-world applicability is demonstrated through case studies in healthcare, autonomous driving, and strategic decision-making. The findings reveal that while AI systems perform well at the data, information, and knowledge levels, they encounter difficulties in achieving wisdom, which necessitates normative reasoning, human-centered design, and long-term foresight. The paper advocates for integrating ethical and philosophical insights, particularly Aristotelian phronesis, into AI architectures. By restructuring AI development around the DIKW model, this study proposes a pathway toward systems that are not only efficient and predictive but also transparent, equitable, and focused on societal well-being