arXiv subjects

Rune Møberg Jacobsen

Publications and source records attributed to Rune Møberg Jacobsen.

4 recordsLinked to original sources

arXiv2026

Understanding, Challenging, and Demystifying Perceptions of Gig Worker Vulnerabilities

Across service domains, platform-based gig workers often face a wide range of severe yet hidden vulnerabilities, including opaque pay practices, illusions of flexibility, health and safety risks, and privacy violations. To the general public and inexperienced workers such latent vulnerabilities remain largely unknown and concealed by intentional platform design that gives illusions of adequate labor protections, or $\textit{myths}$. This study examines how workers perceive (and shift their beliefs away from) five commonly held misconceptions regarding gig worker vulnerabilities. In $Phase~I$, crowdworkers ($N~=~236$) rated their agreement with five common myths surrounding vulnerabilities in gig work:$~227$ of them believed one or more myth(s). In $Phase~II$, we challenged these workers to defend their views by presenting an expert- or LLM-generated counterargument. Our findings show workers' underexposure to personal and shared vulnerabilities of gig work, revealing a knowledge gap where persuasive interventions can scalably raise awareness around such hidden labor conditions. We reflect on the effectiveness of different persuasion strategies and discuss implications for promoting more accurate public perceptions that support collective bargaining of workers' rights.

cs.HC↗

arXiv2025

Confirmation Bias as a Cognitive Resource in LLM-Supported Deliberation

Large language models (LLMs) are increasingly used in group decision-making, but their influence risks fostering conformity and reducing epistemic vigilance. Drawing on the Argumentative Theory of Reasoning, we argue that confirmation bias, often seen as detrimental, can be harnessed as a resource when paired with critical evaluation. We propose a three-step process in which individuals first generate ideas independently, then use LLMs to refine and articulate them, and finally engage with LLMs as epistemic provocateurs to anticipate group critique. This framing positions LLMs as tools for scaffolding disagreement, helping individuals prepare for more productive group discussions.

cs.HC↗

arXiv2025

The Impact of a Chatbot's Ephemerality-Framing on Self-Disclosure Perceptions

Self-disclosure, the sharing of one's thoughts and feelings, is affected by the perceived relationship between individuals. While chatbots are increasingly used for self-disclosure, the impact of a chatbot's framing on users' self-disclosure remains under-explored. We investigated how a chatbot's description of its relationship with users, particularly in terms of ephemerality, affects self-disclosure. Specifically, we compared a Familiar chatbot, presenting itself as a companion remembering past interactions, with a Stranger chatbot, presenting itself as a new, unacquainted entity in each conversation. In a mixed factorial design, participants engaged with either the Familiar or Stranger chatbot in two sessions across two days, with one conversation focusing on Emotional- and another Factual-disclosure. When Emotional-disclosure was sought in the first chatting session, Stranger-condition participants felt more comfortable self-disclosing. However, when Factual-disclosure was sought first, these differences were replaced by more enjoyment among Familiar-condition participants. Qualitative findings showed Stranger afforded anonymity and reduced judgement, whereas Familiar sometimes felt intrusive unless rapport was built via low-risk Factual-disclosure.

cs.HC↗

arXiv2025

Distributed Cognition for AI-supported Remote Operations: Challenges and Research Directions

This paper investigates the impact of artificial intelligence integration on remote operations, emphasising its influence on both distributed and team cognition. As remote operations increasingly rely on digital interfaces, sensors, and networked communication, AI-driven systems transform decision-making processes across domains such as air traffic control, industrial automation, and intelligent ports. However, the integration of AI introduces significant challenges, including the reconfiguration of human-AI team cognition, the need for adaptive AI memory that aligns with human distributed cognition, and the design of AI fallback operators to maintain continuity during communication disruptions. Drawing on theories of distributed and team cognition, we analyse how cognitive overload, loss of situational awareness, and impaired team coordination may arise in AI-supported environments. Based on real-world intelligent port scenarios, we propose research directions that aim to safeguard human reasoning and enhance collaborative decision-making in AI-augmented remote operations.

cs.HC↗