TrustLens-AI: A Multidimensional Analytical Framework for Measuring Public Trust in AI-Generated News

Abstract-This study presents TrustLens-AI, a multidimensional framework for quantifying public trust in AI-generated news, combining perception metrics with sentiment and contextual adjustments. Using survey data from 62 participants, we calculated a composite TrustLens Score (TLS) from five weighted dimensions: content quality, transparency cues, ethical alignment, engagement sentiment, and prior AI exposure, adjusted for platform reputation and consumption frequency. The mean TLS was 64.3 ± 12.7 (0–100 scale). Disclosure of AI authorship increased mean trust ratings from 3.1 to 3.8 (1–5 scale), while respondents with prior AI news exposure scored 9.4 points higher in TLS on average. Decision tree analysis (R² = 0.74, MAE = 0.42) identified transparency cues and content quality as the strongest predictors, followed by ethical alignment. These results highlight that targeted transparency strategies and ethical oversight can substantially improve perceived credibility, helping bridge the trust gap between AI-generated and human-authored journalism.