Photo by — @helvetiica

AI Strategy, Data Centres and the Climate Crisis

How Do We Create a Strategy for Climate Friendly and Sustainable Artificial Intelligence?

I want to undertake literature study of the current anthropological works within infrastructure relating to technology and supply chain of artificial intelligence (AI). In particular the empirical research by anthropologists relating to both software and hardware can be connected to the infrastructure that enables and produces AI solutions in the applied sense. Within this notion you find anthropological studies of data centres, education, machine learning, mining, logistics and policy-making. My intention is to map out situations relating to the production of artificial intelligence.

One must consider the materiality of AI in the infrastructures of its supply chain with relevant empirical studies with a discussion of corporate science in technology. Auditing algorithms has become a question of ecology as much as societal concerns in the conceptual framework of sustainability, and in the pragmatic technological progress in future proposed it may be required for impact assessments of artificial intelligence to include an ethnographic component.

It would be pertinent to ask why one should study artificial intelligence (AI), and an easy answer would be that anthropologists study what interests people. There has been a rush into this field of research as well as discussions about AI for a very long time, yet it has grown immensely in popularity due to recent innovations particularly in machine learning techniques enabling the detection within images as well as optimisation for given queries ‘beating’ humans at many tasks. One should not of course be satisfied with such an answer, and then it begs the question: so what? The answer to this in a somewhat pragmatic way is: the climate crisis. Particularly since this answer can be used to big questions in many industries nowadays it is not a sufficient answer. However if we consider that there is a widespread lack of understanding of how artificial intelligence and technology infrastructure is a physical consumption as much as digital proliferation then perhaps we could argue that a study in this direction is justified as an appropriate query for an anthropologist.

Anthropologists know perhaps better than many other disciplines due to our proximity to people in our studies over a length of time that there is a discrepancy between stated behaviours in large quantified data and the practice occurring in companies, state and NGOs. The field of technology within software and hardware is an area to study for anthropologists, and collaboration in this direction to increase awareness of issues to be addressed or ways to implement can be important for a more habitable planet with greater diversity of people, animals and nature. The main point here in this essay is learning more and getting a broader understanding of the issue to address it in a constructive way within an anthropological framework that at some point in the close foreseeable future can engender greater collaboration with fields in the natural sciences involved in development, critique and operation of technology with computer science being of particular interest in this direction.

The ontological life-world of certain young people these days, at least many in the streets, is imagined through the cardboard signs of a burning planet — our world is melting away and our habitat as humanity is severely threatened.

At this point it is apparent that we must change our emissions and we are already facing severe consequences in a variety of places in the world. The planet is hotter now on average than at many other times in recorded history.

This is a short reflection note.

This is #500daysofAI and you are reading article 252. I am writing one new article about or related to artificial intelligence every day for 500 days. My current focus for 100 days 200–300 is national and international strategies for artificial intelligence.



AI Policy and Ethics at Student at University of Copenhagen MSc in Social Data Science. All views are my own.

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