The Adoption of Artificial Intelligence Into Journalism Practice: Perspectives From the Ghanaian Media Industry

Abstract

The adoption of Artificial Intelligence (AI) technology in journalism globally is characterized by a significant disparity, with Western countries exhibiting more widespread and advanced usage compared to non-Western countries. As a result, research on AI's application in journalism has predominantly focused on developed economies, creating a substantial knowledge gap and scarcity of studies exploring AI's use in journalism in developing countries. This study addresses this gap by examining the current state of AI deployment in Ghana's media industry, its potential benefits and risks, and the challenges hindering its adoption. The study was anchored on Rogers' adoption-diffusion theory and van Dijk's digital dichotomy theory. Based on eighteen in-depth interviews with journalists selected through purposive and snowball sampling, this study reveals that AI is being leveraged to improve newsroom efficiency, but a significant digital divide persists. While some newsrooms actively adopt AI, others lag behind. The adoption of AI is expected to yield both positive outcomes, such as enhanced efficiency and innovative broadcasting, and negative outcomes, including diminished human creativity and potential disinformation. The high cost of deployment, inadequate data, and poor internet connectivity are barriers to AI adoption in Ghana's media industry.



Author Information
Samuel Adefioye, University of Education, Winneba, Ghana

Paper Information
Conference: MediAsia2024
Stream: Journalism

This paper is part of the MediAsia2024 Conference Proceedings (View)
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To cite this article:
Adefioye S. (2024) The Adoption of Artificial Intelligence Into Journalism Practice: Perspectives From the Ghanaian Media Industry ISSN: 2186-5906 – The Asian Conference on Media, Communication & Film 2024: Official Conference Proceedings (pp. 405-419) https://doi.org/10.22492/issn.2186-5906.2024.33
To link to this article: https://doi.org/10.22492/issn.2186-5906.2024.33


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Posted by James Alexander Gordon