Journal of Information & Systems Management M. Krishnamurthy 16 2 2026 https://doi.org/10.6025/jism/2026/16/2/56-74 https://www.dline.info/jism/fulltext/v16n2/jismv16n2_2.pdf This study presents a comprehensive data-driven investigation of global Artificial Intelligence (AI) ethics frameworks and governance patterns through machine learning and visual analytics techniques. Analyzing a curated dataset of 112 AI policy documents published between 2016 and 2019 across public-sector institutions, private corporations, and non-governmental organizations worldwide, the research employs K-Means clustering, Principal Component Analysis, hierarchical clustering, and correlation analysis to systematically examine ethical priorities embedded within 25 conceptual dimensions. Results identify three distinct document clusters: Technical and Governance Leaders emphasizing accountability, privacy, and technical explainability; Thematic or Generalist Frameworks articulating broad ethical principles with limited operational guidance; and Comprehensive Adopters reflecting mainstream consensus on fairness, transparency, and safety. Sectoral analysis reveals divergent ethical emphases: private organizations prioritize technical implementation and reliability, NGOs emphasize human rights and equity, while public institutions focus on regulatory accountability and governance. Temporal analysis demonstrates a significant shift in discourse from speculative concerns about existential AI risks to practical governance challenges, including algorithmic bias, explainability, and transparency. Correlation analysis further indicates that ethical principles function as interconnected governance ecosystems rather than isolated values, with strong associations between transparency-accountability, privacy-safety, and fairness-human rights. Geographical variations highlight the influence of cultural, legal, and institutional contexts on ethical prioritization. The findings underscore the transition of AI ethics from abstract declarations toward operational, measurable governance frameworks, while emphasizing the need for interdisciplinary collaboration and globally coordinated approaches to develop trustworthy, socially responsible AI ecosystems.