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.

Large Language Models (LLMs) are sparking debates about creativity, intellectual property, and artistic integrity. This paper focuses on creativity, defined as consensual agreement among domain experts. It presents an inductive analysis of seven semi-structured interviews with professional playwrights who engaged in a longitudinal project with the aim of writing a theatre script using commercial systems. Overall, participants regarded LLMs as unsuitable for playwrighting. However, they enjoyed the experience and identified utility for editorial tasks and brainstorming. A significant obstacle was associated with the politics embedded in LLMs. Not only did these systems avoid a language that could offend sensibilities, but they also refused to engage in taboos and conflicts, which are the core of dramaturgy. Other system features (speed, exploitation, and unpredictability) were sometimes considered conducive and sometimes detrimental to creativity. Participants experienced difficulties and tried to build common ground by trial and error. Often, this strategy evolved into role play: the playwright instructed the LLM to enact characters. The interaction provided hints of inspiration and fostered suspension of disbelief and ontological reflection. However, it often led to technology rejection. Comparing and contrasting our insights with related work, we conclude by opening new directions for research at the boundaries of HCI and AI.

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.