Neoliberalism has become orthodoxy in the present, erasing competing paradigms and alternative imaginings. Chile's radical Cybersyn project from 1971 to 1973 offers a departure point for an alternative path, albeit one that was abruptly and violently extinguished. We revisit this moment by fine-tuning AI language models on the words and writing of Salvador Allende, the Chilean President, and Stafford Beer, the cyberneticist who helped to design the project. We conduct interviews with these simulated personas, focusing on how their revolutionary ideas might be taken up in the present. We then use an AI model to generate five-year-plans from 1973 to the present, simulating an alternate history guided by Cybersyn and a progressive agenda. We frame these interventions as socialist infrastructuring that cultivates a more expansive socialist imagining. This work is not about the viability of planned economies, but about the 'inspirability' of exploring other value-systems in the present, allowing us to break out of our future-on-rails to envision alternative ways of organizing economy and society.

This paper describes an AI tutoring system built upon two psycho-social theoretic constructs: Hegelian recognition and Freudian psychodynamics. Two related interventions are proposed: recognition-enhanced prompts that instruct an AI tutor to treat the learner as an autonomous subject, and a multi-agent ego/superego architecture where an internal critic reviews tutor output. The paper also describes the nature of the human/machine relationship involved in this research itself, employing a reflexive methodology: Claude Code (Opus 4.5/4.6) builds, evaluates, and documents the AI tutor by authoring a companion scientific paper - a process termed "vibe scholarship" - in conjunction with human prompting and suggestion, which is itself documented and analyzed. The companion paper, included as appendix, reports a factorial evaluation across three generation models (DeepSeek V3.2, Haiku 4.5, Gemini Flash 3.0), finding recognition-enhanced prompts produce large, model-independent improvements (d=1.34-1.92) through a calibration mechanism that raises the floor of tutor performance. This result, significant in itself, is combined with the qualitative reflections in this paper to consider impacts of AI on the delicate dynamics of student / teacher and assistant / researcher relations.

While generative AI image models are both powerful and problematic, public understanding of them is limited. In this essay, we provide a framework we call Unmaking AI for investigating and evaluating text-to-image models. The framework consists of three lenses: unmaking the ecosystem, which analyzes the values, structures, and incentives surrounding the model's production; unmaking the data, which analyzes the images ad text the model draws on, with their attendant particularities and biases; and unmaking the output, which analyzes the model's generative results, revealing its logics through prompting, reflection, and iteration. We apply this framework to the AI image generator Stable Diffusion, providing a case study of the framework in practice. By supporting the work of critically investigating generative AI image models, “Unmaking AI” paves the way for more socially and politically attuned analyses of their impacts in the world.

COVID-19 resulted in global restrictions on migration, with pronounced consequences in Australia, where the resettlement of refugees was significantly curtailed from March 2020. This research, comprising a third phase in an ongoing study, seeks to understand the broader implications of these restrictions on family separation and reunion among resettled refugees in Australia. Employing a mixed-method approach of surveys and family interviews conducted in late 2021, we explore various themes the pandemic's effects on family reunion, concerns about family still `at home', maintaining social connections, post-migration difficulties and financial hardships. The findings reveal a negative impact of COVID-19 on refugees' ability to reunite with families, with evidence pointing differences between gender, visa category, and language group/ethnicity. The research underscores the need for innovative approaches in resettlement to address the negative impacts of family separation and for governments to expedite family reunion pathways to alleviate isolation and uncertainty among resettled refugees.

Artificial intelligence (AI) is increasingly integrated into care systems, yet little is known about how care service providers perceive and respond to AI in their service provision in the context of supporting culturally and linguistically diverse migrants with disabilities. This study draws on an intersectionality-informed, arts-based research approach to explore how care providers make sense of AI, with attention to how their perceptions are shaped by social identities, professional experiences, and media narratives. A one-act play, constructed from data collected through participatory workshops with 15 care providers, illustrates that participants engage with AI as a relational, emotionally charged, and socially situated phenomenon. Their understanding reflected intersecting experiences of racialization, migration, gender, and labor precarity, as well as exposure to dominant media portrayals of AI. Their narratives showed a mix of fear, ambivalence, and cautious optimism rooted in concern about job security and loss of relational care, alongside hopes that AI might enhance accessibility and reduce human error. The play-based format captured the dialogic, affective, and embodied dimensions of participants' meaning-making, challenging technocratic and disembodied ways of knowing about AI and care. Findings suggest that inclusive and reflective spaces are critical for care providers to engage meaningfully with AI technologies and that intersectionality must inform the design, governance, and implementation of AI in care settings.

Generative AI (GenAI) offers powerful possibilities but also introduces significant socio-cultural, political, and environmental issues. It is therefore imperative that researchers and practitioners, responsible for shaping these technologies, develop the critical capabilities necessary to engage with GenAI systems in creative, responsible, and ethical ways. Recent discourse on ‘unmaking’ indicates that this can be a useful paradigm for us to better understand GenAI. In this workshop we provide a framework for “Unmaking AI” — introducing participants to different GenAI models; real-world examples of researchers using these models in practice; and conduct hands-on unmaking AI activities, using bespoke design cards, created for experimentation and reflection. The workshop requires no prior participant knowledge or engagement with GenAI systems. Through this workshop we aim to bring together a community of researchers and practitioners interested in shaping discourse about critically and creatively unmaking AI and collaboratively develop tools, resources, and readings to support them.

Everyday consumer technologies are increasingly integral to autonomy, mobility, and social participation among people with disabilities and migrants from culturally and linguistically diverse (CaLD) backgrounds. However, these technologies often remain inaccessible and exclusionary at the intersection of these identities. This study examined how CaLD migrants with disabilities engage with everyday consumer technologies using participatory and intersectionality-informed approaches. This article focuses on Stage Two of the Autonomy, Diversity & Disability: Everyday Practices of Technology project, funded by the Australian Research Council industry partnership grant (LP: 190900099), which involved individual interviews, creative workshops, guided discussions, post-workshop reflections, and the co-creation of AI-generated e-books. Drawing on three case studies, the analysis identified three key findings: (1) participants experienced a disproportionate burden in navigating digital accessibility and advocating for their needs; (2) generative AI perpetuated biases and misrepresentations of intersecting identities; and (3) participants actively used everyday consumer technologies to foster agency, learning, caregiving, and cultural connection. Through sustained participatory engagement, the researchers identified methodological parameters to inform future disability-inclusive, participatory, and intersectionality-informed research.

Built heritage has been both subject and product of a gaze that has been sustained through moments of colonial fixation on ruins and monuments, technocratic examination and representation, and fetishisation by a global tourist industry. We argue that the recent proliferation of machine learning and vision technologies create new scopic regimes for heritage: storing and retrieving existing images from vast digital archives, and further imparting their own distortions upon this gaze. We introduce the term 'machinic gaze' to conceptualise the reconfiguration of heritage representation via artificial intelligence (AI) models. To explore how this gaze reframes heritage, we deploy an image-text-image pipeline that reads, interprets, and resynthesizes images of several UNESCO World Heritage Sites. Employing two concepts from media studies-heteroscopia and anamorphosis-we describe the reoriented perspective that machine vision systems introduce. We propose that the machinic gaze highlights the artifice of the human gaze and its underlying assumptions and practices that combine to form established notions of heritage.

The world is experiencing an accelerating digital transformation. One aspect of this is the implementation of algorithmic decision-making, often supported by Artificial Intelligence. Algorithmic systems have the potential to change the meaning of critical elements of the engagement between social workers and people who need support. At the same time, social workers are increasingly being required by their agencies to integrate such abstracting technologies and techniques evermore comprehensively into their professional practice. This integration also creates tensions for training future social workers. It requires on the one hand directly responding to an old but intensifying tension between training students technically, and, on the other, providing the pedagogical tools for finding some critical distance from those same technologies and techniques. The article works through this tension of technique/critique, and a second associated tension between deploying technologies of mediated engagement and being present 'in the room' (presence/mediation). The consequences of how both tensions are handled, we suggest, are being intensified through the technologization of service-delivery. The article suggests ways of approaching a technical-critical pedagogy that responds to this complexity by being practice-embedded and theoretically engaged.

Large Language Models (LLMs) are promising analytical tools. They can augment human epistemic, cognitive and reasoning abilities, and support 'sensemaking', making sense of a complex environment or subject by analysing large volumes of data with a sensitivity to context and nuance absent in earlier text processing systems. This paper presents a pilot experiment that explores how LLMs can support thematic analysis of controversial topics. We compare how human researchers and two LLMs GPT-4 and Llama 2 categorise excerpts from media coverage of the controversial Australian Robodebt scandal. Our findings highlight intriguing overlaps and variances in thematic categorisation between human and machine agents, and suggest where LLMs can be effective in supporting forms of discourse and thematic analysis. We argue LLMs should be used to augment, and not replace human interpretation, and we add further methodological insights and reflections to existing research on the application of automation to qualitative research methods. We also introduce a novel card-based design toolkit, for both researchers and practitioners to further interrogate LLMs as analytical tools.

This chapter experiments with ways computational vision interprets and synthesises representations of the Anthropocene. Text-to-image systems such as MidJourney and StableDiffusion, trained on large data sets of harvested images and captions, yield often striking compositions that serve, alternately, as banal reproduction, alien imaginary and refracted commentary on the preoccupations of Internet visual culture. While the effects of AI on visual culture may themselves be transformative or catastrophic, we are more interested here in how it has been trained to imagine shared human, technical and ecological futures. Through a series of textual prompts that marry elements of the Anthropocenic and Australian environmental vernacular, we examine how this emergent machinic gaze both looks out, through its compositions of futuristic landscapes, and looks back, towards an observing and observed human subject. In its varied assistive, surveillant and generative roles, computational vision not only mirrors human desire but articulates oblique demands of its own.

This paper explores use of multiple large language model (LLM) agents to simulate complex, dynamic characters in dramatic scenarios. We introduce a drama machine framework that coordinates interactions between LLM agents playing different 'Ego' and 'Superego' psychological roles. In roleplay simulations, this design allows intersubjective dialogue and intra-subjective internal monologue to develop in parallel. We apply this framework to two dramatic scenarios - an interview and a detective story - and compare character development with and without the Superego's influence. Though exploratory, results suggest this multi-agent approach can produce more nuanced, adaptive narratives that evolve over a sequence of dialogical turns. We discuss different modalities of LLM-based roleplay and character development, along with what this might mean for conceptualization of AI subjectivity. The paper concludes by considering how this approach opens possibilities for thinking of the roles of internal conflict and social performativity in AI-based simulation.

While the pandemic highlighted the critical role technology plays in children’s lives, not all Australian children have reliable access to technology. This situation exacerbates educational disadvantage for children who are already amongst our nation’s most vulnerable. In this research project, we carried out a pilot project with three schools in Western Australia, conducting a series of workshops and interviews with students, parents, school staff members, and teachers. Drawing on rich empirical material, we identify key barriers and enablers for digitally inclusive online learning at the individual, interpersonal, organizational, and infrastructural levels. Of particular importance is that technology is only part of this story—an array of social, environmental, and skills “infrastructure” is needed to facilitate inclusive online learning. Building on this finding, we ran a Digital Inclusion Studio to address this holistic set of issues with strongly positive feedback from participants. We conclude with a set of recommendations for stakeholders (parents, schools, government agencies) who wish to support more digitally inclusive learning.

Large Language Models produce sequences learned as statistical patterns from large corpora. In order not to reproduce corpus biases, after initial training models must be aligned with human values, preferencing certain continuations over others. Alignment, which can be viewed as the superimposition of normative structure onto a statistical model, reveals a conflicted and complex interrelationship between language and technology. This relationship shapes theories of language, linguistic practice and subjectivity, which are especially relevant to the current sophistication in artificially produced text. We examine this practice of structuration as a two-way interaction between users and models by analysing how ChatGPT4 redacts perceived `anomalous' language in fragments of Joyce's Ulysses and the new linguistic practice of prompt engineering. We then situate this alignment problem historically, revisiting earlier postwar linguistic debates which counterposed two views of meaning: as discrete structures, and as continuous probability distributions. We discuss the largely occluded work of the Moscow Linguistic School, which sought to reconcile this opposition. Our attention to the Moscow School and later related arguments by Searle and Kristeva casts the problem of alignment in a new light: as one involving attention to the social structuration of linguistic practice, including structuration of anomalies that, like the Joycean text, exist in defiance of expressive conventions. These debates around the communicative orientation toward language can help explain some of the contemporary behaviours and interdependencies that take place between users and LLMs.

Globally we are living through a continuing transition into the ‘information age’, where information and communication technology has transformed almost every aspect of people’s lives. The COVID-19 pandemic arguably accelerated this change. For refugees, as with other people, digital inclusion is arguably critical to social inclusion. This article seeks to better understand the digital inclusion of refugees during the COVID-19 pandemic, using data from two phases of research conducted in 2020 and 2021 with refugees who had recently resettled in Australia. Digital inclusion was mapped against three domains – access, affordability, and literacy – used in the annual Australian Digital Inclusion Index. Our research makes three contributions: it examines levels of digital inclusion among recently arrived refugees; it explores the relation of these levels to social links and bonds; and discusses differences within the sample according to gender, age, language group and type of digital inclusion.

From the deployment of chatbots as procurement negotiators by corporations such as Walmart to autonomous agents providing ‘differentiated chat’ for managing overbooked flights, synthetic media are making the world of logistics their ‘natural’ habitat. Here, the coordination of commodities, parts and labour design the problems and produce the training sets from which ‘solutions’ can be synthesised. But to what extent might synthetic media, surfacing via platforms such as Midjourney and OpenAI, be understood as logistical media? This paper charts a selective genealogy of synthetic media from early attempts to synthesise human sensory capacities in order to cybernetically integrate them into computational circuits. We see this integration as a pre-emption of their (computational) logistical coordination. It then details media experiments with ‘ChatFOS’, a GPT-based bot tasked with developing a logistics design business. Using its prompt-generated media outputs, we assemble a simulation and parody of AI’s emerging functionalities within logistical worlds. In the process, and with ‘human-in-the-loop’ stitching, we illustrate how large language models become media managers overseeing image prompts, graphical design, website code, promotional copy and investor pitch scenarios. The processes and methods of producing speculative scenarios via ChatFOS lead us to consider how the media of logistics and the logistics of media are increasingly enfolded. We ask: what can a (practice-based) articulation of this double-becoming of logistics and synthetic mediality tell us about the politics and aesthetics of contemporary computation and capital?

This article focuses on uses and experiences of everyday sensory technologies by racially and ethnically diverse persons with disabilites, bringing our research to the junction of critical technology studies, migration studies, and critical disability studies. We draw on a large-scale qualitative project that involves new and second-generation migrants with disabilities from a socio-economically disadvantaged area in Sydney, Australia. Findings show the negotiated exchanges of inclusion and exclusion that disabled people from diverse racial and ethnic minority backgrounds encounter with sensory and other technologies. While such technologies have rightfully been criticized for their roles in the surveillance, regulation, exclusion, and financialization of disability and ethnically diverse groups, these negotiations show how processes of agency, awareness, and peer support produce and in turn benefit from encounters with technology in complex ways. We argue the continued emergence of automation warrants both critique and cautious ongoing experimentation.

Drawing from the resources of psychoanalysis and critical media studies, in this article we develop an analysis of large language models (LLMs) as ‘automated subjects’. We argue the intentional fictional projection of subjectivity onto LLMs can yield an alternate frame through which artificial intelligence (AI) behaviour, including its productions of bias and harm, can be analysed. First, we introduce language models, discuss their significance and risks, and outline our case for interpreting model design and outputs with support from psychoanalytic concepts. We trace a brief history of language models, culminating with the releases, in 2022, of systems that realise ‘state-of-the-art’ natural language processing performance. We engage with one such system, OpenAI's InstructGPT, as a case study, detailing the layers of its construction and conducting exploratory and semi-structured interviews with chatbots. These interviews probe the model's moral imperatives to be ‘helpful’, ‘truthful’ and ‘harmless’ by design. The model acts, we argue, as the condensation of often competing social desires, articulated through the internet and harvested into training data, which must then be regulated and repressed. This foundational structure can however be redirected via prompting, so that the model comes to identify with, and transfer , its commitments to the immediate human subject before it. In turn, these automated productions of language can lead to the human subject projecting agency upon the model, effecting occasionally further forms of countertransference. We conclude that critical media methods and psychoanalytic theory together offer a productive frame for grasping the powerful new capacities of AI-driven language systems.

Data sharing partnerships are increasingly an imperative for research institutions and, at the same time, a challenge for established models of data governance and ethical research oversight. We analyse four cases of data partnership involving academic institutions and examine the role afforded to the research partner in negotiating the relationship between risk, value, trust and ethics. Within this terrain, far from being a restraint on financialisation, the instrumentation of ethics forms part of the wider mobilisation of infrastructure for the realisation of profit in the big data economy. Under what we term `combinatorial data governance' academic structures for the management of research ethics are instrumentalised as organisational functions that serve to mitigate reputational damage and societal distrust. In the alternative model of `experimental data governance' researchers propose frameworks and instruments for the rethinking of data ethics and the risks associated with it - a model that is promising but limited in its practical application.