Abstract as per original application
(English/Chinese): |
While five finalists in the 2024 Pulitzer Prize for Journalism acknowledged using artificial intelligence (AI) in their work and Bloomberg’s Cyborg system generates thousands of articles within milliseconds, a paradox emerges from this “AI-turn” in journalism: do users perceive AI-synthesized news as more or less biased than human journalism? Competing arguments emerge: users may view AI as objective due to its rule-based operation and freedom from human prejudices, or conversely, as suspect due to algorithmic opacity and its inability to exercise contextual judgment. In politically polarized environments, even balanced reporting can be perceived as biased—a phenomenon known as hostile media perception (HMP)—which leads to news disengagement, selective avoidance, and diminished trust in the broader media environment.
This project examines public perceptions of and responses to AI-synthesized news—AI-powered news curation applications—from socio-psychological and journalism studies perspectives. We examine how source-level (authorship: human vs. AI vs. human-AI hybrid), message-level (editorial transparency disclosure: evidence-based sourcing citations vs. process-based fairness claims), and system-level (platform reach: established commercial application vs. student coursework prototype) factors shape bias perception as stipulated by HMP (Objective 1). Informed by human-computer interaction and self-categorization theory, we explore how this news–perception pathway is moderated by three techno-political dispositions—machine heuristics (mental shortcuts about algorithmic objectivity versus inflexibility), affective polarization (in-group solidarity and out-group animosity), and technological nationalism (belief in one’s nation’s technological superiority)—that condition audience responses to AI-authored content (Objective 2). We also examine how such hostile perception predicts several attitudinal and behavioral outcomes, including news engagement, platform trust, and policy attitudes (Objective 3).
To ground this inquiry in high-stakes environments, this project implements two population-based online experiments in the United States and Hong Kong, using U.S. federal AI regulation (Study 1, U.S. sample) and U.S. semiconductor export controls on (Study 2, U.S. and Hong Kong samples) as test cases. Both topics provide ecological validity through their sustained media coverage, concrete economic impacts, and geopolitical importance.
The findings aim to advance theories in media effects, political psychology, and human-AI interaction (Objective 4), and offer evidence-based insights for media organizations and policymakers (Objective 5). For practitioners, we identify transparency and platform positioning strategies that build credibility. For policymakers, our findings foster democratic media innovation, aligning with the UN Sustainable Development Goals to promote peace, justice, strong institutions, and responsible innovation.
N/A
|
