From Editorial Gatekeeping to Agentic Media Curation: A Conceptual Model for Reinforcement Learning Systems and Societal Accountability

As media production and distribution increasingly integrate recommender systems, generative AI, and conversational interfaces, gatekeeping shifts from a primarily editorial function to a distributed, continuously optimizing socio-technical process. Building on recent work on AI-enabled gatekeeping, automated content pipelines, and platform governance, this paper advances a conceptual model of AI gatekeeping across media production and couples it with an RL-informed agent perspective to clarify where—and how—societal values are operationalized (or displaced) by optimization objectives. The conceptual model decomposes media gatekeeping into an end-to-end pipeline of decision points that apply beyond journalism to creator economies and platform-native formats: (1) creator and source inclusion (who gets visibility and monetization access), (2) topic/format selection and trend detection, (3) content generation and editing (text, audio, image, video), (4) packaging and metadata (titles, thumbnails, tags, captions), (5) ranking/recommendation and feed placement, (6) moderation and safety filtering, and (7) distribution feedback loops (engagement, retention, and revenue signals). Each stage is specified in terms of actors (creators, studios, newsrooms, platforms, vendors), artifacts (training data, prompts, style guides, policies), and outcomes (visibility, framing, attention allocation, and cultural salience). The RL-informed perspective treats AI-mediated media gatekeeping as sequential decision-making under constraints, where agents learn policies that trade off timeliness, quality, novelty, audience satisfaction, and harm minimization. This framing makes explicit a core societal risk: when reward signals are proxied by engagement and watch-time, systems can rationally learn behaviors that amplify outrage, optimize for addictive consumption patterns, privilege already-dominant aesthetics, or systematically under-expose minority creators and niche cultural topics—often without any single decision appearing normatively problematic in isolation. The paper proposes evaluation dimensions aligned with social-science concerns—exposure equity, plurality of cultural representation, provenance and disclosure, and user contestability—paired with RL-relevant diagnostics (reward specification audits, off-policy evaluation, counterfactual exposure tests, and drift monitoring).