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.

The integration of artificial intelligence (AI) into journalism has become a global phenomenon, with China emerging as a notable player in this evolving landscape. Milestones specific to China, such as the introduction of AI news anchors and the landmark case where AI-generated news articles were granted copyright protection, underscore the country’s pioneering role in this domain. This chapter provides a comprehensive overview of Chinese newsrooms’ employment of AI technologies, including the latest generative AI applications, in newsgathering and the production, presentation, distribution, and consumption of news. It traces historical developments and examines the intricate dynamics among state actors, journalists, tech companies, and the public. By contextualizing AI within China’s unique journalistic culture and regulatory framework, this chapter elucidates the distinct characteristics of AI development in the Chinese media sector. It also highlights the implications of these developments for journalism as an institution, both within China and globally. 

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.

Research on climate change has focused on news media’s framing of climate issues. With a shifting media landscape, news media face increased competition from other actors, including climate movement groups. This study examines how credibility and trustworthiness of a source (source ethos) influences attitudes toward information dissemination and collective action on climate change. Respondents (N = 507) were randomly assigned to news stories that varied in source ethos – legacy media (Dagens Nyheter), a conventional climate movement group (Fridays for Future), and a disruptive climate movement group (Extinction Rebellion) – as well as emotional appeals (hope, fear, balanced). While most results were not significant, Fridays for Future seems more effective in shifting attitudes on information dissemination in the balanced and hope conditions. Dagens Nyheter and Fridays for Future influenced attitudes toward collective action in balanced conditions. Findings suggest that source ethos shapes climate perceptions and that different sources play distinct roles in raising awareness and mitigating action.

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. 

This study investigates the impact of Large Language Model (LLM)-based chatbots, specifically in the context of political information retrieval, using the 2024 Taiwan presidential election as a case study. With the rapid integration of LLMs into search engines like Google and Microsoft Bing, concerns about information quality, algorithmic gatekeeping, biases, and content moderation emerged. This research aims to (1) assess the alignment of AI chatbot responses with factual political information, (2) examine the adherence of chatbots to algorithmic norms and impartiality ideals, (3) investigate the factuality and transparency of chatbot-sourced synopses, and (4) explore the universality of chatbot gatekeeping across different languages within the same geopolitical context. Adopting a case study methodology and prompting method, the study analyzes responses from Microsoft’s LLM-powered search engine chatbot, Copilot, in five languages (English, Traditional Chinese, Simple Chinese, German, Swedish). The findings reveal significant discrepancies in content accuracy, source citation, and response behavior across languages. Notably, Copilot demonstrated a higher rate of factual errors in Traditional Chinese while exhibiting better performance in Simplified Chinese. The study also highlights problematic referencing behaviors and a tendency to prioritize certain types of sources, such as Wikipedia, over legitimate news outlets. These results underscore the need for enhanced transparency, thoughtful design, and vigilant content moderation in AI technologies, especially during politically sensitive events. Addressing these issues is crucial for ensuring high-quality information delivery and maintaining algorithmic accountability in the evolving landscape of AI-driven communication platforms.