Toward a governance framework for AI-mediated communication: reframing media ecology in the age of algorithms and generative technologies

Abstract

Artificial intelligence (AI) technology is reshaping the media ecosystem with unprecedented depth and breadth, emerging as a critical topic at the intersection of communication studies, information technology, and social governance. This paper centers on the theme of "AI-driven media ecosystem transformation" and systematically examines it through three analytical dimensions: technological penetration, industrial restructuring, and social impact.

The objective is to elucidate the comprehensive influence of AI on media production, distribution, and consumption patterns, while also addressing the ethical challenges and governance strategies associated with its integration.  The study employs a mixed-methods approach, combining literature analysis, case studies, randomized controlled trials (RCTs), and the AHP-Delphi method. Through a systematic analysis of multi-source data, a "technology-industry-society" triadic research framework is established.

Findings reveal that AI has substantially enhanced the efficiency of content creation and dissemination, yet it has simultaneously intensified the information cocoon effect and raised concerns regarding creative homogenization. At the industrial level, traditional media organizations are evolving into data-driven entities, and business models are diversifying, though platform monopolies are increasingly prevalent. At the societal level, algorithmic usage has triggered issues related to subjectivity erosion and ethical misconduct, underscoring the urgent need for a multi-layered governance mechanism.

This research reveals that while algorithmic recommendations enhance user engagement, they significantly reduce information diversity. Although generative AI lowers the threshold for content creation, it introduces challenges related to copyright and attribution. The intelligent transformation of the media ecosystem must therefore strike a balance between technological efficiency and social responsibility. It establishes a systematic analytical framework, addressing the insufficient attention of existing research to generative AI and the broader creative industry, and provides theoretical references for algorithm governance within the Chinese context. Practically, this study offers strategic guidance to policymakers, media organizations, and the public in navigating the challenges of intelligent media transformation, contributing to the development of a healthier, more diverse, and sustainable media ecosystem.