This study investigates the performance of search engine chatbots powered by large language models in generative political information retrieval. Applying algorithmic accountability as a central theme, this research (a) assesses the alignment of artificial intelligence (AI) chatbot responses with timely political information, (b) investigates the factual correctness and transparency of chatbot-sourced synopses, (c) examines the adherence of chatbots to democratic norms and impartiality ideals, (d) analyzes the sourcing and attribution behaviors of the chatbots, and (e) explores the universality of chatbot gatekeeping across different languages. Using the 2024 Taiwan presidential election as a case study and prompting as a method, the study audits responses from Microsoft Copilot in five languages. The findings reveal significant discrepancies in information readiness, content accuracy, norm adherence, source usage, and attribution behavior across languages. These results underscore the contextual awareness when applying accountability assessment that looks beyond transparency in AI-mediated communication, especially during politically sensitive events.

Scandinavians are among the most datafied citizens in the world. With its digitalised welfare states, Danish, Norwegian, and Swedish e-governance infrastructures collect massive amounts of data about citizens as they search for jobs, apply for building permits, and check school calendars. In this article, we analyse the use of third-party trackers (n = 2,761) on Scandinavian municipal websites (n = 745) between 2007-2023. Mobilising the theoretical framework of universalism, our aim is to understand what kind of cost data tracking constitutes for users of digital government services. Results show that Scandinavian municipal websites are dominated by commercial trackers harvesting citizen data for advertising purposes, particularly those provided by Alphabet and Meta. We conclude that commercial user-tracking on Scandinavian municipal websites does not conform to the principle of universality, proposing 1) that governments ensure transparency of the cost incurred by these websites' data tracking, and 2) that they ban commercial tracking on municipal websites.

This study examines the role of news media in the context of generative AI-enhanced search engines, focusing on the 2024 Taiwan presidential election. Using Microsoft’s Copilot, we conducted a comparative analysis by prompting election news in five languages: English, Traditional Chinese, Simplified Chinese, German, and Swedish. While Copilot uses mainly professional news media, provides quick access to synthesized information, and exhibits source transparency, it frequently creates misinformation and misattributes news sources. The analysis highlights variations in Copilot’s sourcing behavior, showing a strong reliance on English-language sources, particularly those from the UK and US, across different prompting languages. Such reliance raises concerns about the homogenization of information and the marginalization of regional perspectives. The study underscores the critical role and dilemma of news media, which, while serving as authoritative sources in democratic societies, must navigate an increasing AI-mediated information ecosystem to maintain autonomy vis-à-vis powerful technological infrastructures. By evaluating Copilot’s sourcing practices and misinformation prevalence, this research contributes to the discourse on AI’s impact on news dissemination, media diversity, and democratic processes. Specifically, we discuss the consequences of two approaches available to news media to prevent their content from being used without compensation: opting out of crawling (“platform counterbalancing”) or establishing partnerships with AI companies. Current regulatory efforts, including copyright reforms and the EU AI Act, fall short of safeguarding journalism or regulating AI. We propose policy and regulatory recommendations to improve transparency, factual correctness, accuracy in source attribution, and accountability in AI-generated content, supporting informed citizenship in the digital age. 

Digital platforms, with their ever-increasing reach and power, are reshaping many aspects of society. China is experiencing a platformization of society akin to what has occurred in the West. By mapping the algorithmic news distribution field in China, this study shows how the key players (including digital platforms, news organizations, and state regulators) compete, collaborate, and work symbiotically with each other in this changing ecology. We explore a particularly key player-Jinri Toutiao, led by ByteDance (parent company of the popular app TikTok)-as a case that exemplifies the platform logic in China's news ecosystem and how it involves a delicate dance with the public and regulators in a politically restrictive environment. We find that the mutual shaping of platforms and society is not a given but rather a dynamic process. Indeed, we show that Toutiao's tremendous success is partially because of the state's laissez-faire policy in the earlier years, and that its move to assume greater social responsibility is a response to recent tightening of regulations. Amid widespread questions about the role and influence of Big Tech and platforms in society, the case of China enriches our understanding of the platformization of news outside the Western context.

This essay argues that there are overlooked yet important journalistic beliefs, norms, rules and practices regarding, aesthetics, automation, distribution, engagement, identity, and proximity that could be a part of formalized codes of ethics. There are four reasons why these should be formalized. First, making the implicit normative dimensions explicit allow for a shared understanding of journalism, cutting across institutional borders. Second, it promotes a more unified and homogenized understanding of journalism across the institution based on those shared explicit norms (normative isomorphism). Third, it reduces the fuzziness of these codes and sharpens their functions as boundary objects, simplifying the negotiation between journalists and audiences. Fourth, and finally, these implicit codes might be an untapped resource that could make journalism better connect with citizens and increase its legitimacy. The paper offers two main contributions to journalism studies. First, it shows that elements of journalistic practice and culture that seem disparate in fact play similar institutional roles, forming boundary objects as sites of tension where codes are negotiated by different actors. Second, systematizing these informal codes into the style of traditional codes of ethics renders them more visible and could help journalism scholars understand the uneven formation and evolution of journalistic norms. 

Information production, dissemination, and consumption are contingent upon cultural and financial dimensions. This study attempts to find cultures of engagement that reflect how audiences engage with news posts made by either commercial or state-owned news outlets on Facebook. To do so, we collected over a million news posts (n = 1,173,159) produced by 482 news outlets in three Scandinavian countries (Denmark, Norway, and Sweden) and analyzed over 69 million interactions across three metrics of engagement (i.e. comments, likes, and shares). More concretely, we investigate whether the patterns of engagement follow distinct patterns across national boundaries and type of outlet ownership. While we are skeptical of metrics of engagement as markers of specific cultures of engagement, our results show that there are clear differences in how readers engage with news posts depending on the country of origin and whether they are fully state-owned or private-owned outlets.

Journalism and other institutions clash over automated news generation, algorithmic distribution and content ownership worldwide. AI policies are the main mechanisms that establish and organise the hierarchies among these institutions. Few studies, however, have explored the normative dimension of AI in policymaking in journalism, especially beyond the West. This case study inspects the copyright law's impact on AI innovation in newsrooms in the unexamined Chinese context. Using neo-institutional theory and policy network theory, the study investigates the Third Amendment to the Chinese Copyright Law, exemplary court cases regarding automated journalism copyright disputes (such as Tencent v. Yingxun and Film v. Baidu), and other supporting documents. The findings show how China's copyright legal framework separates authorship and ownership; defines "originality" and "creativity" in human-machine collaboration; and prioritises tech companies while undermining journalistic autonomy. We argue that the law's eager embrace of AI may give tech companies an advantage over news organisations that do not necessarily have a strategy to adopt AI. Moreover, it favours state-owned, resource-rich official media over the private sector. An implication of this shifting power dynamic is the possibility of privately owned news media being marginalised, resulting in even stronger state control over media production and information flow.