From Adoption to Accountability in Generative AI Use: A Risk-Based Framework

Abstract

This study analyses the challenges and benefits of the increasing use of generative artificial intelligence (Gen-AI) models into academic, professional, and industrial writing practices. The rapid adoption of Gen-AI in academic, professional and industrial writing has significantly enhanced efficiency in ideation, translation, summarization, drafting, and editing, reducing both time and cost. But the adoption has outpaced the development of consistent ethical and governance standards.  The use of Gen-AI models introduces substantial ethical and governance challenges including the generation of fabricated evidence, citation inaccuracies, violations of intellectual property, professional misrepresentation, and the erosion of human cognitive and compositional competencies. Existing responses to the Gen-AI challenges in literature remain fragmented, leaving uncertainty about acceptable assistance, disclosure, authorship and accountability regarding Gen-AI use. This study addresses the gaps by proposing an Integrated Risk-Based Framework for Responsible Generative AI Writing (IRF–GenAIW)  for the responsible and accountable use of Gen-AI. Findings from the systematic literature review analysis of literature indicate that current institutional approaches are inconsistent and concentrate on issues of plagiarism and academic misconduct, with inadequate consideration of broader applications in professional and industrial sectors. The contribution of this study is that it shifts attention from whether Gen-AI is used to whether it is used responsibly and with integrity. It develops a risk-based Gen-AI framework that guides disclosure, verification, human oversight, authorship, confidentiality and accountability in Gen-AI use across various academic, professional and industrial sectors.