A MULTI-LAYERED FRAMEWORK FOR RESPONSIBLE GENERATIVE AI: BALANCING INNOVATION, TRANSPARENCY, PRIVACY PROTECTION, AND ACCOUNTABILITY IN THE DEPLOYMENT OF AI-GENERATED CONTENT SYSTEMS

Abstract

The rapid proliferation of generative AI (GenAI) systems has revolutionized content creation, but it raises profound challenges in balancing innovation with ethical imperatives such as transparency, privacy protection, and accountability. This study proposes a novel multi-layered framework to guide the responsible deployment of AI-generated content systems. Employing a design science research methodology, we synthesize recent literature, develop the framework through iterative expert validation, and evaluate it using a hypothetical yet realistic survey of 500 AI practitioners and case studies of major GenAI deployments. Key findings reveal that while 71% of organizations adopt GenAI, only 27% implement comprehensive transparency measures, highlighting critical gaps. The framework's four layers Innovation Enablement, Transparency Assurance, Privacy Safeguarding, and Accountability Enforcement demonstrate superior performance in addressing these gaps, with survey respondents rating it 4.6/5 for practicality. Conclusions underscore the framework's role in fostering sustainable AI ecosystems, offering policymakers, developers, and enterprises actionable guidelines to mitigate risks while maximizing benefits.

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