Mapping Human-AI Teaming in Risk Analysis: Role Evolution, Thematic Landscape, and Governance Mechanisms From News Media.

Human-artificial intelligence (AI) teams are increasingly embedded in risk analysis, yet news reports repeatedly portray meaningful oversight as fragile when review conditions are poorly designed. This study examines how public discourse has portrayed the roles, failure modes, and governance mechanisms of human-AI teaming in risk analysis. Drawing on 184,282 AI-related Factiva news articles (1956-2025), we use a funnel-shaped mixed-methods design to identify 3571 articles describing human-AI interaction in risk-analytic contexts. Structural topic modeling identifies 20 thematic clusters and their temporal and cross-domain dynamics, whereas LLM-assisted inductive thematic analysis produces five mirror-mapped pairs of failure modes and governance mechanisms: opacity and explainability, bias and auditing, agency erosion and cognitive friction, systemic fragility and oversight and liability, and accountability vacuums and institutional governance. The analysis shows that media discourse often follows an AI-predicts-human-decides paradigm and portrays formal human-in-the-loop requirements as insufficient without deliberate interaction design.