Data-Driven Investigation of Global Artificial Intelligence Ethics Frameworks and Governance Patterns
M. Krishnamurthy
Journal of Information & Systems Management, Vol. 16, No. 2, 2026.
DOI: https://doi.org/10.6025/jism/2026/16/2/56-74
Link: https://www.dline.info/jism/fulltext/v16n2/jismv16n2_2.pdf
Abstract:
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.