Global Ethical AI Data Standard: A framework for regulatory harmonization

Maryala, Bhanu Teja Reddy (2025) Global Ethical AI Data Standard: A framework for regulatory harmonization. World Journal of Advanced Engineering Technology and Sciences, 15 (2). pp. 2836-2841. ISSN 2582-8266

The Global Ethical AI Data Standard (GEADS) introduces a comprehensive framework addressing fundamental challenges in artificial intelligence data governance. Production AI systems frequently exhibit inadequate provenance documentation and questionable consent mechanisms, while data annotation practices often lack transparency and fair compensation. The regulatory environment presents significant fragmentation across jurisdictions, with limited convergence in core data protection principles between major frameworks like GDPR and CCPA. Informed consent mechanisms fundamentally fail in AI contexts because traditional notice-and-consent frameworks cannot anticipate how machine learning might derive unexpected insights from data. GEADS addresses these challenges through a three-tier classification system based on data sensitivity and potential impact, implementing core principles of Transparent Provenance, Contextual Consent, Annotator Protections, Derivative Accountability, and Proportional Governance. The framework demonstrates high regulatory compatibility across jurisdictions while maintaining minimal technical overhead. Implementation results show significant improvements in consent comprehension, annotation quality, and ethical issue detection during development phases. Organizations adopting GEADS report reduced compliance costs, fewer regulatory inquiries, and improved stakeholder trust. The framework bridges policy requirements with technical implementation, creating actionable guidelines that harmonize diverse regulatory approaches while introducing AI-specific provisions that address unique challenges in machine learning data governance.