In this essay, we argue that, unlike previous changes in digital media technologies over the past few decades, this AI “turn” in journalism forces us to rethink journalism’s identity and its relationship with audiences. While AI complicates and challenges some existing professional, social, political, and economic structures, it also offers new ways to realize desired journalistic objectives that were previously considered to be impractical, if not impossible. Drawing on four orienting ideas—adoption and hype, power and dependency, audiences and democratic implications, and education and empowerment—we unpack the implications of this AI turn in journalism and the consequences for the future of the journalistic field.

Amid concerns over algorithmic gatekeeping and the power of digital platforms to serve as engines for polarization and disinformation, we examined the performance of algorithmic recommendation systems as news intermediaries by crowdsourcing search results about newsworthy topics. This study offers a crowdsourced audit of the recommendations made by Google, Google News, Facebook, YouTube, and Twitter to ideologically, geographically, and demographically diverse U.S. participants (N = 1,598), examining the extent to which search algorithms on major platforms personalized results and drove traffic to particular kinds of websites. The findings of our cross-platform analysis show that rather than creating filter bubbles, the sorting mechanisms on platforms strongly homogenize exposure to information, at least among its top results. This effect was evident across search terms and platforms. At the same time, each platform prioritizes different types of content, with professionally produced news dominant on some platforms but not others, and politically conservative mainstays like Fox News being particularly recurrent.

Coverage of artificial intelligence and algorithms has been largely examined by scholars studying countries in the Global North that have historically supported conditions for critical journalism and watchdog journalistic role performances. However, it is unclear if the findings from such work would be applicable to authoritarian contexts that do not share those conditions. This study addresses this gap through a textual analysis of 23 journalistic investigations of AI and algorithmic systems published in Chinese news media between 2019 and 2023. We found that Chinese journalists were critical of multiple aspects of algorithmic systems and called for urgent AI governance at the nation-state level. Despite the technical nature of the issue, those journalists overwhelmingly employed traditional reporting techniques to uncover political and economic intersections—namely those resulting from the rise of tech companies and the algorithms they implement. Chinese journalists simultaneously performed the roles of being watchdogs and loyal facilitators by highlighting the risks posed by private platforms and their algorithms while casting the state as a protector and responsible steward of technological development. The study thus highlights the intricate roles necessary to perform critical journalism in authoritarian contexts, and the possibilities that the case of AI permits.

This study incorporates the concepts of transparency, innovation, and collaboration within a broader analytic lens of trust-building infrastructure and applies that lens to an examination of the use of GitHub by 124 prominent news outlets over more than a decade. It finds that (a) their use of GitHub is not widespread but several outlets do actively use it; (b) they use GitHub to open-source a mixture of technologies and journalistic materials; (c) their introductory project documentation routinely includes at least partial amounts of both ambient and disclosure forms of transparency, but rarely exhibits participatory transparency; (d) collaboration is almost non-existent in the vast majority of their repositories; and (e) there has been a decline in their use of GitHub and the collaboration affordances within their repositories in recent years. The study extends the transparency literature by adapting key concepts to journalism-adjacent infrastructure and offers empirical evidence about the innovativeness of open-source technologies originating from prominent news organizations and the amount of collaboration that occurs around them. This builds to an intervention that raises some questions about the direct impact of open-source repositories as trust-building infrastructure while drawing attention to less-considered but nevertheless useful performative functions that such infrastructure enables.

Images have become a key vehicle for communicating climate change, especially in a visually oriented social media ecosystem. However, few studies have examined the ways in which climate change is visually communicated on those platforms. This study addresses that gap by examining more than 2 million images appearing alongside tweets containing #climatechange, identifying the types of images different stakeholders share and the amount of engagement those images elicit. It highlights differences in the image types that are published frequently (e.g., textual visualizations), the image types that users prefer to engage with (e.g., protest images), and the impact of bots and a cyclical communication pattern keyed to focusing events. These findings are then evaluated through a conceptual framework of media logics, which helps highlight some of the distinctions between (news) media logic and social media logic—and their emerging hybridization—within the context of climate change communication.