ETHICAL AND LEGAL GOVERNANCE OF GENERATIVE AI IN SCIENTIFIC RESEARCH: POLICY IMPLICATIONS

Abstract

Purpose: This systematic review examines the evolving landscape of ethical and legal governance frameworks for generative artificial intelligence (AI) in scientific research, analyzing current regulatory approaches and their policy implications across global jurisdictions. The study aims to identify key challenges, assess existing governance mechanisms, and provide evidence-based policy recommendations for responsible AI integration in research practices.

Methods: A comprehensive systematic review was conducted using multiple databases including PubMed, Scopus, Web of Science, and policy repositories. The search covered literature published between 2022-2025, focusing on peer-reviewed articles, policy documents, regulatory frameworks, and institutional guidelines. A total of 247 documents were initially identified, with 89 meeting inclusion criteria after screening. Thematic analysis was employed to categorize findings into regulatory frameworks, ethical principles, legal challenges, and policy recommendations.

Results: The analysis reveals significant heterogeneity in governance approaches across jurisdictions, with the EU AI Act leading comprehensive regulatory efforts while other regions adopt sector-specific guidelines. Key challenges identified include: research integrity concerns (hallucinations, bias), intellectual property disputes, authorship attribution, data protection compliance, and environmental sustainability. Current frameworks inadequately address the dynamic nature of generative AI capabilities and fail to provide unified standards for scientific applications.

Conclusions: Harmonized international governance frameworks are urgently needed to address the complex ethical and legal challenges posed by generative AI in scientific research. Policy recommendations include establishing mandatory disclosure requirements, developing AI literacy programs, creating specialized oversight bodies, and implementing adaptive regulatory mechanisms that can evolve with technological advancement.