Narratives of artificial intelligence frame public perceptions and expectations, and have a performative role, potentially leading to increased attention and resource allocation, acceptance of AI, or resistance to the technology. However, research on AI narratives frequently produces generalized and decontextualized accounts. This paper argues for closer examination of the specific processes that shape AI narratives in particular contexts. To explore this, nine AI-related exhibitions held in German museums from 2022 to 2023 were analyzed. The study draws on interviews with curatorial (...) teams and exhibition materials to investigate what narratives of AI museums are formed and how those relate to the dominant AI narratives described in previous research. It is shown that curators are critical of dominant AI narratives and aim to deconstruct common myths and present more nuanced portrayals of AI. Yet, they also face challenges in balancing this critical stance with the need for creating engaging, accessible exhibitions. The analysis reveals that while some curatorial strategies successfully counter AI stereotypes, others fail or inadvertently reproduce mainstream imaginaries. Curators discover that avoiding problematic understandings of AI in the way they had initially intended is challenging or even impossible. In other cases, when elements of mainstream AI discourse resurface in museum exhibits despite the critical stance of the curators, they remain unexamined, or are regarded as insignificant and inconsequential. (shrink)

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 (...) the ensuing challenges. This article advocates for a re-evaluation of existing deterministic frameworks, emphasizing the need for adaptive decision-making mechanisms in computational systems. By dissecting the Executioner Paradox, the aim is to foster a robust discussion on evolving deterministic frameworks to accommodate the dynamic nature of self-modifying code, thereby contributing a forward-looking lens to the discourse on computational systems amidst advancing AI. (shrink)

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 (...) report lower expected well-being. (shrink)