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- Convenors:
-
Alexandru Balasescu
(Royal Roads University, Victoria, Canada)
Sandra Fernandez
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- Format:
- Panel
- Sessions:
- Monday 6 June, -
Time zone: Europe/London
Short Abstract:
Two opposite tendencies shape our world: the fetishism of technology as both ultimate saviour and existential threat, and the "return" to nature facing Climate Change. This panel explores tensions and contradictions in the way we define nature, and how it is re-shaped in AI discourse and practices.
Long Abstract:
Two apparently opposite tendencies are shaping the world today: the universal appeal of technology as both ultimate saviour and existential threat for humans, and the centrality of the return to nature facing the challenges of Climate Change. "Artificial? Naturally!" is a panel inviting the participants to revisit the points of convergence of these tendencies, în order to propose an anthropological perspective of the complex dynamic at the intersection of nature, technology and culture as it is reshaped by the generalisation of Artificial Intelligence (AI).
Some of the leading, but not limiting, themes could be:
Optimal Nature: what is the view of "nature" proposed in AI approaches on Climate Change solutions? what happens when we relate to nature as a measurable entity in a quasi digitized world?
Indigenous and non-AI knowledge: How does the deployment of AI systems intersect with other social and cultural systems of practices and knowledge production? How indigenous forms of knowledge could inform, transform, and reform AI algorithm design?
Natural Bodies: How do different conceptions of the ideal human body interact and shape AI design and application? And how does the use and abuse of AI reflect back on our understanding of the human body itself? How is the technological body racialized/ genderized/ classified? How does this reflect our relationship with categories such as "nature"?
Any form of submissions are welcome, from ethnographies of AI design, implementation, adoption and adaptation, to reflections on AI applications for Climate Change action.
Accepted papers:
Session 1 Monday 6 June, 2022, -Matt Lukacz (Columbia University)
Paper short abstract:
This paper argues that the implementation of technologies such as AI in conservation practice requires a collaboration between conservation practitioners, digital innovators, and ethnographers. To make this point, I draw on the studies of collaboration from the literature on critical data studies.
Paper long abstract:
In light of the accelerating biodiversity loss, conservation biologists attempt to use all methods and tools at their disposal to alleviate the crisis. In this vein, the field has begun to critically reflect on the role and the promise of technology. One of the prominent technologies which some have argued could have potential benefits for conservation is artificial intelligence (AI). But what role can or should AI play in conservation biology? My paper argues that the implementation of technologies such as AI in conservation practice requires a collaboration between conservation practitioners, digital innovators, mathematicians, and social scientists. To make this point, I draw on the lessons about generative modes of collaborations between ethnographers and data scientists from the literature on critical data studies, and the framework of “critique and contribute” proposed by Gina Neff from the Oxford Internet Institute and her colleagues (2017). I draw attention to the absence of political ecologists in imagining and designing conservation technology, and call for their increased participation in these processes. Yet in order to make such collaboration feasible, it is important to revisit the critiques of quantitative sciences in political ecology. Therefore, the paper calls for political ecologists to revisit their conception of quantitative sciences not as tools of governmentality embedded solely in the power/knowledge nexus, but as productive (albeit imperfect) methods of environmental governance. This reevaluation is intended to promote a possibility for ethnographers to engage with computational tools and their designers as to make them more fair, just, transparent, and equitable.
Alexandru Balasescu (Royal Roads University, Victoria, Canada)
Paper short abstract:
We want to believe that AI can somehow give us mastery over nature by simply measuring and managing it. But how did we come to think that nature is separated from us, measurable, and manageable? This paper will explore the implications of relating to the environment as a quantifiable category.
Paper long abstract:
We want to believe that AI can somehow give us mastery over nature by simply measuring and managing it. But how did we come to think that nature is separated from us, measurable, and manageable? And how is this conviction reflected in the way we generate knowledge about, and understand nature today? What are we consciously or unconsciously leaving aside so we can build our predictable models that regularly fail?
This way of knowing, that seems natural throughout modernity, operates a fundamental break in the flow of things, so to speak. Mostly, in order to measure anything, first we recreate it according to our interest, and separate it from the rest of the system. In other words, this type of thinking extracts a fragment from a phenomenon, renders it measurable, and then re-presents it as being the truth about that phenomenon.
What happens when we relate to nature as a measurable entity in a quasi digitized world?
This paper will explore the questions above and propose a possible alternative to the overarching "knowing by measurement" paradigm.