Ideology is power exerted by language, whether generated by humans or machines.It manifests in the biases produced by Large Language Models (LLMs), reflecting power relations between users and providers.Following this assumption, we engaged in an artistic critique of how ChatGPT produces ideology.We conducted two experiential workshops with 20 artists, analysing their reflections and interactions through ethnographic inquiry and formal linguistic analysis grounded in Thompson's account of ideology.The artists reported that both commercial goals and debiasing efforts constrain artistic expression, reinforcing dominant cultural values.To support critical engagement, we introduce a framework that maps recurring linguistic patterns in ChatGPT outputs to ideological modes of operation.This framework offers HCI practitioners an analytical tool to interrogate the socio-political implications of LLMs in design contexts.Our findings highlight the role of artists as critical agents in socio-technical transitions and call for interdisciplinary approaches to language technology critique.

The application of Machine Learning is driven by the techno–capitalist struggle for productivity across various domains, including the creative industry. Sociological research has demonstrated how technology–induced temporality introduces challenges at the individual and societal levels. Art creativity conflicts with speed and mass production. This paper describes Isotta, a critical artefact combining a Mignon typewriter and a Language Model to spark discussion about ML–induced acceleration. Fourteen artists evaluated Isotta in an interview study, and semiotics was used as the analytical lens. Results exposed ideological assumptions around the consequences of technology in the writing realm. We discuss these insights in the context of interactive design in times of techno–capitalistic acceleration. Our findings highlight the significance of temporal factors in designing generative writing interactions and underscore how complex societal challenges can be approached in design through the contrast–eliciting property that outdated technologies offer when juxtaposed with contemporary technologies.

The design of robust and trustworthy Generative AI (GenAI) requires a deep understanding of the agencies emerging from human interactions with them. To contribute to this goal, we retrospectively studied an art project involving a visual artist, a computer scientist, an artistic director, and a generative model (GPT-2). The model was fine-tuned with trip reports describing the experience of eating psychedelic mushrooms. Building on agential realism, we analysed the co-performance between the artist and the model as their agency moved along the choreographer-performer continuum. Results reveal ontological surprises, leading to the proposal of entangled authorship to de-individualise the production of knowledge from a More Than Human perspective. The paper illustrates how art can expose different forms of relationships, challenging the idea of GenAI as just a tool that simplifies or replaces human labour. We conclude by emphasising the transformational potential of GenAI for novel modes of engagement between humans and machines.