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Artificial intelligence (AI) is transforming how news is produced, curated, and consumed, challenging traditional gatekeeping theories rooted in human editorial control. We develop a robust theoretical framework to reconceptualize gatekeeping in the AI era. We integrate classic media theories—gatekeeping, agenda-setting, and framing—with contemporary insights from algorithmic news recommender systems, large language model (LLM)–based news writing, and platform studies. Our review reveals that AI-driven content curation systems (e.g., social media feeds, news aggregators) increasingly mediate what news is visible, sometimes reinforcing mainstream agendas, according to Nechushtai & Lewis, while, at other times, introducing new biases or echo chambers. Simultaneously, automated news generation via LLMs raises questions about how training data and optimization goals (engagement vs. diversity) act as new “gatekeepers” in story selection and framing. We found pervasive Simon’s theory that reliance on third-party AI platforms transfers authority from newsrooms, creating power dependencies that may undercut journalistic autonomy. Moreover, adaptive algorithms learn from user behavior, creating feedback loops that dynamically shape news diversity and bias over time. Drawing on communication studies, science & technology studies (STS), and AI ethics, we propose an updated theoretical framework of “algorithmic gatekeeping” that accounts for the hybrid human–AI processes governing news flow. We outline key research gaps—including opaque algorithmic decision-making and normative questions of accountability—and suggest directions for future theory-building to ensure journalism’s core values survive in the age of AI-driven news.
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Stored only when both papers are in the corpus — 29 citations exist upstream.
Papers that cite the same references — the relational fallback while embeddings are still backfilling.
Imported from OpenAlex (CC0) — nothing here is generated.