Source-linked AI summary
Without journalists, there is no journalism: the social dimension of generative artificial intelligence in the media
Simón Peña-Fernández, Koldobika Meso-Ayerdi, Ainara Larrondo-Ureta, Javier Díaz-Noci
TL;DR
Journalism’s adoption of AI raises questions about job insecurity, platform dependence, and whether automated content can serve professional and social goals. This review synthesizes 223 studies and finds that collaborative journalist–AI models and audience acceptance are central to AI’s media future.
Problem
Generative AI has rekindled debate over whether adoption presents an opportunity or threat to journalism, amid concerns about journalists’ job insecurity.
Method
The article systematically reviewed 223 Spanish- and English-language studies from 2000 onward, analyzing AI’s social impact on media, professionals, and audiences.
Results
Audiences perceive automated texts as similarly credible and high-quality to journalists’ texts, while readability and enjoyableness favor journalists.
Takeaways & Limitations
AI in journalism should be evaluated by its contribution to professional and social goals through complementary, non-substitutive collaboration with journalists.
Takeaways & Limitations
Existing automated texts have mainly covered peripheral, highly structured domains such as weather and stock-market information.
Abstract
from arXiv · showhide
The implementation of artificial intelligence techniques and tools in the media will systematically and continuously alter their work and that of their professionals during the coming decades. To this end, this article carries out a systematic review of the research conducted on the implementation of AI in the media over the last two decades, particularly empirical research, to identify the main social and epistemological challenges posed by its adoption. For the media, increased dependence on technological platforms and the defense of their editorial independence will be the main challenges. Journalists, in turn, are torn between the perceived threat to their jobs and the loss of their symbolic capital as intermediaries between reality and audiences, and a liberation from routine tasks that subsequently allows them to produce higher quality content. Meanwhile, audiences do not seem to perceive a great difference in the quality and credibility of automated texts, although the ease with which texts are read still favors human authorship. In short, beyond technocentric or deterministic approaches, the use of AI in a specifically human field such as journalism requires a social approach in which the appropriation of innovations by audiences and the impact it has on them is one of the keys to its development. Therefore, the study of AI in the media should focus on analyzing how it can affect individuals and journalists, how it can be used for the proper purposes of the profession and social good, and how to close the gaps that its use can cause.
1. Introduction
The introduction frames AI as a broad, socially consequential set of technologies transforming media and journalism while creating ethical, professional, and human challenges. The article therefore examines AI’s social dimension across media development, effects on professionals, and consequences for audiences.
- AI’s scope: AI comprises seven computer-science sub-areas and increasingly affects tasks and domains previously considered exclusively human.These sub-areas include machine learning, computer vision, speech recognition, natural language processing, automatic planning, expert systems, and robotics.
- Promises and risks: AI may improve democracy and public services, but biased data, automated decisions, and privacy intrusion can deepen social inequalities and create ethical repercussions.Risks include widening gender, race, and class gaps through biased or outdated training data and design errors.
- Media applications: In media, AI supports applications across the information process, while generative systems can produce news narratives with limited or no human intervention.Applications range from analyzing consumption habits and tracking social-media trends to identifying disinformation and moderating content.
- Journalism’s human dimension: AI challenges journalism’s intrinsically human character by introducing new human–technology relationships and potential job insecurity for journalists.The profession’s human dimensions include emotional and social engagement with covered topics and intimacy with readers’ emotions and desires.
- Article aim: The article identifies journalism’s main AI-era challenges across business and industrial development, media professionals, and audiences.Its focus is the social dimension of journalism in relation to these three essential aspects.
2. AI and journalism
AI-driven content automation has existed in media for decades but has historically remained limited and concentrated in structured, speed-oriented domains. Generative AI has expanded its development and use, prompting media organizations to adopt it systematically and debate whether it benefits or threatens journalism.
- Historical development: Content automation has existed in media for at least four decades, although its scope has remained limited.The passage presents automation as longstanding rather than entirely new in journalism.
- Conceptual framing: Media AI is commonly associated with automated content, despite applications and theoretical developments becoming highly varied, especially during the last decade.Automated content is the dominant media framing of AI, but it does not encompass the field’s full range of uses.
- Organizational adoption: News organizations and agencies have developed automated production initiatives, usually with technology companies, using predetermined templates to connect information.Examples include The New York Times, The Washington Post, Le Monde, Reuters, and Associated Press.
- Initial applications: Automated content has primarily targeted weather, financial, and sports information because structured databases support efficient extraction and speed-oriented reporting.Chatbots are also included among these applications.
- Generative AI: The popularization of generative AI has taken content automation further, with media outlets reporting systematic AI use in producing news material.The passage identifies systems such as ChatGPT, Dall-E, and Midjourney and describes an unprecedented degree of development and implementation.
3. Methodology
The study conducted a systematized bilingual literature review of automated and AI-related journalism research from 2000 onward. It prioritized empirical work on AI’s social dimension, excluded purely technical or nonjournalistic studies, and analyzed a final sample of 223 texts quantitatively and qualitatively.
- Literature review: The review covered Spanish- and English-language research since 2000 using “automated journalism,” “robot journalism,” and related AI terminology.The search also included functional synonyms and related definitions such as algorithms and artificial intelligence.
- Analysis: Results were categorized using quantitative reference counts and qualitative analysis of topics identified through summaries and keywords.
- Selection criteria: 223 texts formed the final sample after prioritizing empirical studies on AI’s social dimension and excluding exclusively technical or nonjournalistic research.The prioritized studies addressed generative AI and the opinions of audiences, professionals, and media managers.
4. Results
AI adoption in the media is constrained by economic, legal, ethical, and editorial concerns, while journalists negotiate threats to their roles with opportunities for more meaningful work. Audience responses show that automated objective content can resemble human journalism, but authorship and genre shape trust and acceptance.
- Media implementation: Media managers identify commercial viability, development investment, and reader acceptance as central concerns for content automation.AI development costs are very high, including for large media, while proprietary development is not always necessary to meet organizational objectives.
- Media implementation: Implementation is further complicated by legal responsibility, diffuse authorship, intellectual-property protection, and generative AI’s challenge to journalism’s human authorship.Human intervention and content creation are described as key assets for journalistic companies competing with large information-distribution platforms.
- Journalists: Journalists perceive AI as threatening employment, symbolic capital, and the integrity of the profession, particularly through narratives of replacement by “robot journalists.”These concerns include social questioning of journalists’ intermediary role and the threat of job loss anticipated by publishers.
- Journalists: Conversely, AI could free journalists from repetitive tasks, support more complex writing and research, and create new roles requiring technical collaboration and hybrid training.Proposed adaptations include supervising generated content and developing specifically human capacities such as curiosity, skepticism, and critical thinking.
- Journalists: Journalists favor a complementary relationship with AI that preserves professional values, maintains control across news production, and reinforces creativity, listening, and sourcing.The ethical and normative values of journalism must be integrated into automated content and journalists’ work.
- Audiences: Audiences often find automated objective journalism difficult to distinguish from human writing, but authorship expectations reduce trust in nonobjective automated content.Automated content can be perceived as efficient and credible, yet its acceptance depends on genre and whether readers know it was produced automatically.
5. Conclusions and discussion
AI in journalism should be understood as a cultural and social technology whose value lies not in its capabilities alone, but in advancing professional and social goals. Its adoption brings efficiency and new opportunities, while raising concerns about platform dependence, professional disruption, bias, transparency, privacy, and social harm.
- General framework: AI adoption in media is industry-led, emphasizing technological capabilities, business models, and optimization over the consequences of adaptation.The paper argues that journalism requires a shift toward assessing social effects rather than treating technology as an end in itself.
- Social and ethical challenges: AI is dual-use and can reproduce biases, vulnerabilities, and risks, so media adoption must address quality, transparency, privacy, information disruption, social development, and the gaps its use may create.The paper calls for evaluating how AI affects people and journalists and how it can serve professional purposes and social good.
- Media organizations: For media organizations, AI can improve efficiency and enable opportunities such as personalized content, but costly implementation increases dependence on large technology platforms.Limited capacity to develop proprietary tools widens the technical gap between large and small media players and challenges organizational, institutional, and professional values.
- Journalists and professionals: Journalists face fears of replacement and professional undermining, yet automation of routine work can increase cognitive value and refocus journalism on interpretation, creativity, criticism, and asking questions.The paper therefore reinforces hybrid, collaborative, and complementary relationships between journalists and AI rather than substitutive ones.
- Audiences: Audiences generally perceive automated content as similarly credible and only slightly different in quality from journalist-produced content, although readability and authorship effects remain relevant.Existing automated texts have mainly concerned structured domains such as weather and stock-market information, while interpretive texts remain a frontier for broader acceptability.