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To understand how AI is fundamentally political, we need to go beyond neural nets and statistical pattern recognition to instead ask what is being optimized, and for whom, and who gets to decide. Computational reason and embodied work are deeply interlinked: AI systems both reflect and produce social relations and understandings of the world. At a fundamental level, AI is technical and social practices, institutions and infrastructures, politics and culture.
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Once we connect AI within these broader structures and social systems, we can escape the notion that artificial intelligence is a purely technical domain. In this sense, artificial intelligence is a registry of power. And due to the capital required to build AI at scale and the ways of seeing that it optimizes AI systems are ultimately designed to serve existing dominant interests. In fact, artificial intelligence as we know it depends entirely on a much wider set of political and social structures. AI systems are not autonomous, rational, or able to discern anything without extensive, computationally intensive training with large datasets or predefined rules and rewards. Rather, artificial intelligence is both embodied and material, made from natural resources, fuel, human labor, infrastructures, logistics, histories, and classifications. In contrast, I argue that AI is neither artificial nor intelligent. When specific algorithmic techniques are the sole focus, it suggests that only continual technical progress matters, with no consideration of the computational cost of those approaches and their far-reaching impacts on a planet under strain. If AI systems are seen as more reliable or rational than any human expert, able to take the “best possible action,” then it suggests that they should be trusted to make high-stakes decisions in health, education, and criminal justice.
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If AI is defined by consumer brands for corporate infrastructure, then marketing and advertising have predetermined the horizon. “Ideally, an intelligent agent takes the best possible action in a situation.” 2Įach way of defining artificial intelligence is doing work, setting a frame for how it will be understood, measured, valued, and governed.
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“Intelligence is concerned mainly with rational action,” they claim. 1 In one of the most popular textbooks on the subject, Stuart Russell and Peter Norvig state that AI is the attempt to understand and build intelligent entities. If you ask experts in deep learning, they might give you a technical response about how neural nets are organized into dozens of layers that receive labelled data, are assigned weights and thresholds, and can classify data in ways that cannot yet be fully explained. Let’s ask the deceptively simple question, What is artificial intelligence? If you ask someone in the street, they might mention Apple’s Siri, Amazon’s cloud service, Tesla’s cars, or Google’s search algorithm. Aiming to foster the fruitful exchange of expertise and perspectives across fields to help us rise to this critical challenge, opinions expressed do not necessarily represent the views of the OECD.
#AI ACTIONS COSTS EXAMPLE SERIES#
This excerpt is part of a series in which experts and thought leaders - from around the world and all parts of society - address for the OECD the COVID-19 crisis, discussing and developing solutions now and for the future. Used with permission of the publisher, Yale University Press. Adapted from Atlas of AI: Power, Politics and the Planetary Costs of Artificial Intelligence.