Date of Award
6-19-2026
Document Type
Thesis
Degree Name
Radio-Television, MSMC
First Advisor
Dustin Sullivan
Committee Members
Galen Perkins; Timothy Arquitt
Abstract
Generative Artificial Intelligence (GenAI) continues to be a divisive topic in the media industry and there are concerns about technical challenges that may affect GenAI’s trustworthiness and reliability. As such, fears of degraded public trust in media products and services have arisen, and while we have a tendency to trust machines, it is clear that public attitudes toward automated decision-making are contentious. Previous research on machine cues found that they are associated with increased perceived objectivity and willingness to disclose information. Conversational GenAI may reduce political polarization, yet many people remain worried about risks such as bias, hallucinations, improper attribution, authorial integrity, and misinformation. Thematic analysis found that human oversight, future development, transparency, fairness, worker enhancement, and accountability were all prevalent patterns of recurrence, repetition, and forcefulness. These concepts led to the synthesis of three themes: “Optimism,” “Ethics and Trustworthiness,” and “Education and Enhancement.” While analysis found insufficient evidence to claim that GenAI erodes media trust broadly, it highlights meaningful risks from premature or opaque adoption in news and other credibility-dependent media which could lead to an erosion of trust. It is recommended that measures to encourage the reduction of overreliance on GenAI, improving GenAI literacy for media professionals, using GenAI to enhance creative workers’ abilities, and disclosing GenAI use in content production to protect media integrity should be further developed and incorporated.
Rights Management
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Recommended Citation
Barnett, Reace, ""Using A Sledgehammer To Crack A Nut" A Proposal to Re-frame The Media Industry's Perspective on Generative AI" (2026). Student Theses and Dissertations. 1195.
https://arch.astate.edu/all-etd/1195
