Navigating GenAI Ethics: A Review of Its Regulatory Landscape and a Proposed Framework

• Generative AI (GenAI) poses unique ethical and regulatory challenges due to its creative nature, requiring distinct governance beyond traditional AI frameworks
  
• The proposed GenAI Governance Framework integrates 11 principles across a six-stage lifecycle, emphasizing responsible development, transparency, and safety
  
• Effective GenAI oversight demands collaboration among developers, businesses, policymakers, and users, with ongoing adaptation to legal, cultural, and technological shifts
  

This paper presents a structured framework to guide the ethical governance of Generative AI systems, acknowledging that GenAI’s creative capacity introduces risks and complexities not adequately addressed by traditional AI standards. Aboitiz Data Innovation (ADI) argues for a lifecycle-based approach that integrates ethical principles at every phase—from problem definition and data acquisition to deployment and monitoring. The paper classifies governance principles into three categories: responsible development and use, transparency and accountability, and safety and oversight. Each principle is mapped to specific development stages to ensure GenAI systems are aligned with societal values throughout their lifecycle.

The framework distinguishes ethical use from harmful and unethical practices by offering concrete examples and drawing attention to lessons learned from past failures. It stresses that without deliberate ethical oversight, GenAI systems are likely to perpetuate bias, violate privacy, and inflict societal harm. To counter this, the paper introduces stakeholder-specific roles and engagement strategies across development stages, ensuring that responsibilities are clearly distributed among developers, regulators, businesses, and end users. Transparent communication, stakeholder alignment, and mechanisms for continual feedback are emphasized as essential components for building trust and maintaining accountability.

The framework also explores how its application varies across industries, such as education, manufacturing, energy, and banking. In each sector, the ethical risks and opportunities differ, from student misuse and job displacement to enhanced automation and fraud detection. Real-world use cases underscore the value of domain-specific implementation of GenAI ethics, particularly in fostering innovation while safeguarding human interests. ADI stresses that with proper governance, GenAI can augment human capability without compromising fairness, security, or public trust.

Looking ahead, the paper underscores the need for regulatory evolution and international collaboration. It outlines current GenAI-related regulations in regions like the EU, US, China, and Canada, and urges organizations to actively shape future standards. As GenAI becomes more autonomous and its applications more pervasive, emerging ethical issues—such as misinformation, creative ownership, and societal influence—must be continuously evaluated. The proposed framework is presented not only as a practical roadmap but also as a living model for ethical alignment, stakeholder cooperation, and future-readiness in GenAI development.

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