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  1. “Your friendly AI assistant”: the anthropomorphic self-representations of ChatGPT and its implications for imagining AI.Karin van Es & Dennis Nguyen - forthcoming - AI and Society:1-13.
    This study analyzes how ChatGPT portrays and describes itself, revealing misleading myths about AI technologies, specifically conversational agents based on large language models. This analysis allows for critical reflection on the potential harm these misconceptions may pose for public understanding of AI and related technologies. While previous research has explored AI discourses and representations more generally, few studies focus specifically on AI chatbots. To narrow this research gap, an experimental-qualitative investigation into auto-generated AI representations based on prompting was conducted. Over (...)
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  • Navigating technological shifts: worker perspectives on AI and emerging technologies impacting well-being.Tim Hinks - forthcoming - AI and Society:1-11.
    This paper asks whether workers’ experience of working with new technologies and workers’ perceived threats of new technologies are associated with expected well-being. Using survey data for 25 OECD countries we find that both experiences of new technologies and threats of new technologies are associated with more concern about expected well-being. Controlling for the negative experiences of COVID-19 on workers and their macroeconomic outlook both mitigate these findings, but workers with negative experiences of working alongside and with new technologies still (...)
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  • The Executioner Paradox: understanding self-referential dilemma in computational systems.Sachit Mahajan - forthcoming - AI and Society:1-8.
    As computational systems burgeon with advancing artificial intelligence (AI), the deterministic frameworks underlying them face novel challenges, especially when interfacing with self-modifying code. The Executioner Paradox, introduced herein, exemplifies such a challenge where a deterministic Executioner Machine (EM) grapples with self-aware and self-modifying code. This unveils a self-referential dilemma, highlighting a gap in current deterministic computational frameworks when faced with self-evolving code. In this article, the Executioner Paradox is proposed, highlighting the nuanced interactions between deterministic decision-making and self-aware code, and (...)
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