In recent years, news media has been greatly disrupted by the potential of technologically driven approaches in the creation, production, and distribution of news products and services. Artificial intelligence (AI) has emerged from the realm of science fiction and has become a very real tool that can aid society in addressing many issues, including the challenges faced by the news industry. The ubiquity of computing has become apparent and has demonstrated the different approaches that can be achieved using AI. We analyzed the news industry’s AI adoption based on the seven subfields of AI: (i) machine learning; (ii) computer vision (CV); (iii) speech recognition; (iv) natural language processing (NLP); (v) planning, scheduling, and optimization; (vi) expert systems; and (vii) robotics. Our findings suggest that three subfields are being developed more in the news media: machine learning, computer vision, and planning, scheduling, and optimization. Other areas have not been fully deployed in the journalistic field. Most AI news projects rely on funds from tech companies such as Google. This limits AI’s potential to a small number of players in the news industry. We made conclusions by providing examples of how these subfields are being developed in journalism and presented an agenda for future research.

With the increasing adoption of artificial intelligence (AI) technologies in the news industry, media organizations have begun publishing guidelines that aim to promote the responsible, ethical, and unbiased implementation of AI-based technologies. These guidelines are expected to serve journalists and media workers by establishing best practices and a framework that helps them navigate ever-evolving AI tools. Drawing on institutional theory and digital inequality concepts, this study analyzes 37 AI guidelines for media purposes in 17 countries. Our analysis reveals key thematic areas, such as transparency, accountability, fairness, privacy, and the preservation of journalistic values. Results highlight shared principles and best practices that emerge from these guidelines, including the importance of human oversight, explainability of AI systems, disclosure of automated content, and protection of user data. However, the geographical distribution of these guidelines, highlighting the dominance of Western nations, particularly North America and Europe, can further ongoing concerns about power asymmetries in AI adoption and consequently isomorphism outside these regions. Our results may serve as a resource for news organizations, policymakers, and stakeholders looking to navigate the complex AI development toward creating a more inclusive and equitable digital future for the media industry worldwide.

Innovation in journalism became an important element to determine the current and future direction of the profession. Through incremental and cumulative transformations over time, because of many obstacles faced inside the newsrooms, journalism has suffered from significant and fundamental changes, including the deployment of data journalism. In Latin America, the practice has seen an increasing expansion in the last years. Nevertheless, there are important technological gaps that limit its development. The present study draws upon literature on data journalism, media management, and sociology, aiming to contribute theoretically to data journalism research. Our findings show that beyond the technological approach, practitioners are relying on data evangelists, collaboration, and audience-centered innovation to produce data storytelling in their newsrooms. On the other hand, these alliances form “homophily” and “endogamy” features that limit the dissemination of the practice, which must consider the potential implications for the social distance of the audiences. It argues that Latin American professionals are distancing themselves from technological determinism to embrace a more audience-centric innovation in newsrooms. Finally, it also states that it is important to take into account those limitations, as they pose obstacles for data journalism innovation research knowledge. The article concludes with an agenda for future research.

Journalism is at a radical point of change that requires organizations to come up with new ideas and formats for news reporting. Additionally, the notable surge of data, sensors and technological advances in the mobile segment has brought immeasurable benefits to many fields of journalistic practice (data journalism in particular). Given the relative novelty and complexity of implementing artificial intelligence (AI) in journalism, few areas have managed to deploy tailored AI solutions in the media industry. In this study, through a mixed-method approach that combines both participant observations and interviews, we explain the hurdles and obstacles to deploying computer vision news projects, a subset of AI, in a leading Latin American news organization, the Argentine newspaper La Nación. Our results highlight four broad difficulties in implementing computer vision projects that involve satellite imagery: a lack of high-resolution imagery, the unavailability of technological infrastructure, the absence of qualified personnel to develop such codes, and a lengthy and costly implementation process that requires significant investment. This article concludes with a discussion of the centrality of AI solutions in the hands of big tech corporations.