A Hybrid Fact-Checking Model: A Methodology for Integrating AI-Based Tools into the Editorial Workflow of a News Portal
Abstract
Amid the exponential growth in the volume and velocity of misinformation, countering contemporary information threats effectively is outside the structural capacity of customary editorial fact-checking models. Manual verification remains the gold standard for quality, yet it lacks the requisite scalability for real-time operation. This lack creates critical vulnerabilities for both media and society. This methodology presents the author’s Hybrid Fact-Checking Model, a thorough sociotechnical framework that integrates artificial intelligence (AI)- based tools into newsroom workflows. It aims for faster verification, broader coverage, and greater accuracy while preserving journalistic ethics and assuring complete human supervision. The methodological novelty is found in a formalizing of three key components: (1) human, machine interaction protocols that operate for a regulation of collaboration between journalists and AI assistants; (2) a task allocation matrix that clearly demarcates zones of responsibility between automated systems that monitor and collect primary data and humans who analyze context, appraise ethics, and adjudicate finally; and (3) a risk management and effectiveness evaluation system that manages risk, evaluates effectiveness, and includes practical methods for minimization of AI hallucinations and algorithmic bias, as well as a set of key performance indicators (KPIs) for a hybrid newsroom. The methodology targets editors and media leaders, serving as a…
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