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.

This paper explores the limitations of using algorithmic management on gig platforms in the Swedish basic service sector. We critically examine how the behaviour of all three actors in the triangular relationship between worker, platform and client deviated from the logic of app-based competitive bidding over the allocation of work; each being agential in bypassing the algorithmic management to engage in out-of-app activity. Our findings suggest the utility and limitations of algorithms differ between sectors of the gig economy. Crucially, the paper contributes to debates on gig worker agency by introducing the concept of opportunistic agency in the analysis of worker non-compliance. In focusing on more instrumental motivations for out-of-app activity our analysis offers a critical perspective on the pervasiveness, omnipotence and universality of algorithmic management.

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. 

This article presents a study of how and to what extent gig workers in Sweden experience a mediatization of work. We contend that previous mediatization research has assumed extensive and unified effects of mediatization, and that previous gig work research has focused on users of large-scale, transnational platforms. We conducted a set of qualitative, semi-structured interviews (N = 28) with Swedish users of four different gig apps (all produced by very small companies active only in Sweden). We analyzed their experiences of mediatization along five dimensions: extension, substitution, amalgamation, accommodation, and datafication. We found that our respondents had much more varied, far less all-encompassing, experiences of mediatization than indicated in previous research. We also found respondents' experiences clearly framed by the smaller size of the local, Swedish gig work companies.

Virtual reality (VR) immerses users in others’ lives, creating empathy and understanding long after the VR scenario has finished. As VR technology has matured, VR scenarios have begun to be used in complex real-world areas such as education, health and organisational change. These scenarios can be of variable technical quality, with limited interactive capacity, unrealistic environments and clunky or absent avatars. In this study, three scenarios related to gender inequality training were constructed with glitches in the core immersive qualities of presence, interactivity and plausibility in order to understand their effect on the immersive experience. Using a multi-step in-depth series of qualitative interviews to examine the whole immersive process, the results show that immersion is not compromised but changed by glitches. Limited interactivity led to uncomfortable interactions that allowed participants to process difficult emotions; implausible situations surfaced buried norms and prejudices; and avatar variation gave rise to a sense presence that also included distance, which gave the user opportunities for critical reflection. These results point towards immersion as a robust and richly textured concept, while interactivity, plausibility and presence can best be understood as dimensions rather than goals. Totally seamless and immersive experiences may not only be utopian but also unnecessary. The glitches in low-end productions can produce powerful communication without expensive technology.