Send message to Authors

Paper short abstract:

Diverse and possibilistic definitions of what AI and of other computational agents are bring unexpected results and uncharted relational patterns. A series of cases across art and design are explored to represent possible psychological, social, cultural, political advantages of cyberdiversity.

Paper long abstract:

When someone today mentions AI, they usually mean some form of deep neural network, trained with large quantities of data to perform a single task. Other definitions had been popular (or, at least, more frequent) in the past, and others could be in the future. In this we may see a pattern with intensive agricolture, where only the cultivars that are most favorable to business prevail and eventually replace the others. The dark side of this parallel is true as well: fragility derives from this scarcity in diversity, as diverse plants (and AIs) thrive in diverse natural (and informational) environments, also providing support to a richer ecosystem that can depend on them. As researchers, technologists, artists and designers we have performed several experiments with AIs and other forms of computational agents created for neighborhoods, rivers, plants, families, autobiographical expressions and more, where these results become evident and impactful. The article describes some of these experiments and draws some conclusions from them, under the form of directions for future research.

Send message to Authors

Paper short abstract:

We explore how an algorithmic system, designed to predict critical illness, is taking shape in a negotiation process between software engineers, project managers, and health professionals. We are particularly interested in the ways professional expertise is articulated, negotiated and transformed.

Paper long abstract:

Development of machine learning based algorithms for the health care sector is currently booming, promising results in terms of diagnostic accuracy, predicting illnesses, screening and triaging patients. Yet, many AI applications in clinical settings are failing due to a neglect of clinical contexts and difficulties with interdisciplinary collaborations. Anthropologists working and conducting research in such interdisciplinary futures-focused spaces can help bridge practical and epistemological gaps and contribute with new understandings of the translational roles that involved participants can take to improve collaborations and ultimately build better technology futures.

xAI-EWS is an explainable AI model, based on machine learning and using data from electronic health records, designed to predict acute critical illness and developed as part of a larger research and innovation project at Regional Hospital Horsens in Denmark involving medical doctors, nurses, data scientists, UX designers and anthropologists, among others. In this paper we explore how the algorithmic system is taking shape in a negotiation process between software engineers, project managers, and health professionals. We are particularly interested in the ways professional expertise is articulated, negotiated and transformed in this process.

The paper builds on long term participant observation in Danish hospitals and interviews and ethnographic conversations with a number of the involved partners, both programmers of the algorithm, data scientists, UX designers and health personnel. Attention to work practices and participatory mapping of the workflows, data infrastructures and use of data and other technologies help us outline the collaboration, and potential misunderstandings and conflicts, between different groups of professionals.

Send message to Author

Paper short abstract:

The image of how the semi-automated technology for type 1 diabetes works, and how patients actually experience it, differ to a great extent. It is time to address these incongruencies in the understanding of care and come to terms with how patients and technology co-create knowledge and experience.

Paper long abstract:

Technological advances in medicine, based on Big Data and AI, allow for faster decision-making processes, precision, radical new definitions of prevention and therapies designed for individuals based on their distinct markers. But not only do these developments come at a certain, often hidden cost – knowledge is being centralized, private companies are endowed with often enormous power while responsibilities shift – but also many of the promises, which are made with regards to AI, simply cannot be kept.

Doing research with type 1 diabetes (T1D) patients on their treatment with (hybrid) closed loop systems in a clinic in Austria, it becomes visible how narratives about the potential of developing technology often clash with individual experiences. T1D is a high maintenance chronic disease that demands the patient’s (or their caregivers) time, effort, expertise, and nerves 24/7. But empowered with the right equipment, patients can now take over full responsibility and consciously manage their bodies – so the story goes. The reality of the lifeworlds of T1D patients often looks rather different: it demands ad-hoc adjustments and reactions to context specific requirements and is indeed much more messy than the standardized technology suggests. From patients experience we can draw a picture of care that differs greatly from the techno-imaginary of the new systems: it is ongoing, open-ended, done by multiple actors (see Mol 2008) and thus allows for radical different future-making options. We need to address these incongruencies in order to work towards the “good lives” of the chronically ill in the future.

Send message to Author

Paper short abstract:

This paper explores AI-assisted technologies used in dementia research for diagnosis and care. I investigate these innovations through the lens of Neuroanthropology, Medical Anthropology, STS, Human-Machine Interaction, and Sociotechnical Theory to envision an Anthropology of AI futures.

Paper long abstract:

Socio-technical studies of neurodegenerative diseases in Futures Anthropology and AI in Medicine are imperative. The societal and economic impacts of dementia are being assessed by public health in the context of demographic factors such as the rate of aging in the global population, progressive changes in the family model, increased responsibility of women in the workforce and as caregivers, immigration dynamics, sociocultural differences, and other global health phenomena. A generalized concern about the sustainability of healthcare and social support systems is now aggravated by the current global crisis brought by the COVID-19 pandemic, which exposed the fragility of existing public health systems and traditional social models of care for the elderly and those who suffer from neurodegenerative conditions and disabilities.

Current biomedical AI technologies mainly target the needs of physicians and healthcare organizations. AI-assisted neuroimaging and face recognition algorithms figure prominently among innovations under development for dementia diagnosis and care. Furthermore, AI research in medicine is considered a strategic endeavor to confront the socio-economic challenges represented by the impact of demographic aging while being viewed as an opportunity for reforms within traditional medical practice. However, I argue that AI developments in dementia research cannot be viewed as an all-encompassing solution to the existing and expected future healthcare crises concerning vulnerable populations affected by neurodegenerative diseases, for the very enterprise of AI innovation carries a myriad of critical issues linked to social, legal, economic, organizational and ethical concerns.

Send message to Author

Paper short abstract:

The rise of social media has often been associated with an increased polarisation of online communities and a decline in mental health. Drawing on three years' research with online communities in the US, UK, and China, I present a vision for a future cyber society that is more compassionate.

Paper long abstract:

'Cyber society' encompasses the new forms of sociality made possible by the integration of human social interaction with major AI components. My respondents in VR routinely use AI-assisted voice modulation or machine learning algorithms that help construct their virtual social spaces and personae. All on social media have their emotions and socialising regulated in some way by search-and-suggestion algorithms, by AI-assisted cameras and filters, by information feeds. Already the social consciousness of the groups I study is a human-AI composite.

Anthropologists are uniquely capable of guiding the development of algorithms that can monitor users’ mental and physical health within a platform. Using empirical ethnographic data we can train algorithms to detect those at risk of, for instance self-harm or substance abuse. These possibilities necessarily require a new ethics, which this paper is intended to help instigate.

In my work researching vaccine hesitancy, I have discerned a runaway process of polarisation that emerges naturally from the high velocity of communication expedited by the algorithms that run social media. Echo chambers. I find that this polarisation plays out unconsciously, with respondents finding themselves adopting views they never thought they could hold as a direct result of the fissioning of social groups. They are pulled into particular patterns of thought and behaviour.

This paper will propose a possible future in which a social network as a whole, as a cybernetic organism, can become, with the help of anthropologists, sentient: aware of itself, of how it feels, and so to develop into emotional maturity.