Atlas of AI, book review: Mapping out the total cost of artificial intelligence

Nancy J. Delong


Atlas of AI: Energy, Politics, and the Planetary Fees of Artificial Intelligence • By Kate Crawford • Yale College Push • 336 webpages • ISBN: 978–300-20957- • £20   

“Talk to forgiveness, not authorization” has lengthy been a guiding theory in Silicon Valley. There is no technological area in which this theory has been a lot more practiced than the device understanding in modern day AI, which depends for its existence on giant databases, nearly all of which are scraped, copied, borrowed, begged, or stolen from the giant piles of knowledge we all emit daily, knowingly or not. But this knowledge is barely ever rigorously sourced with the subjects’ authorization.  

“Mainly because we can,” two sociologists explain to Kate Crawford in Atlas of AI: Energy, Politics, and the Planetary Fees of Artificial Intelligence, by way of acknowledging that their educational institutions are no unique from engineering organizations or federal government businesses in pertaining to any knowledge they come across as theirs for the having to practice and check algorithms. Illustrations or photos come to be infrastructure. This is how device understanding is created. 

All people would like to converse about what AI is very good or harmful for — determining facial pictures, decoding speech commands, driving cars and trucks (not however!). Many want to pour ethics above present-day AI, as if generating policies could change the armed forces funding that has defined its essential mother nature. Couple of want to talk about AI’s true expenditures. Kate Crawford, a senior researcher at Microsoft and a research professor at the College of Southern California, is the exception. 

In Atlas of AI, Crawford starts by deconstructing the popular rivalry that ‘data is the new oil’. Ordinarily, that sales opportunities people to converse about data’s economic value, but Crawford focuses on the simple fact that both are extractive systems. Extraction is mining (as in ‘data mining’ or oil wells), and exactly where mining goes, so stick to environmental injury, human exploitation, and profound society-vast repercussions.  

Crawford underlines this level by heading to Silver Peak, Nevada, to go to the only working lithium mine in the US. Lithium is, of system, a important ingredient in battery packs for everything from smartphones to Teslas. Crawford follows this up by contemplating the widening implications of extraction for labour, the resources of knowledge, classification algorithms, and the country-condition behaviour it all underpins, ending up with the electrical power buildings enabled by AI-as-we-know-it. This way lies Project Maven and ‘signature strikes’ in which, as former CIA and NSA director Michael Hayden admitted, metadata kills people. 

Snake oil

Nonetheless some of this is patently bogus. Crawford traces back the image datasets on which the newest disturbing snake oil — emotion recognition — is centered, and finds they have been created from posed pics in which the subjects have been explained to to offer exaggerated illustrations of emotional reactions. In this situation, ‘AI’ is produced all the way down. Is there, as Tarleton Gillespie requested about Twitter developments, any true human reflection there? 

Even though other engineering textbooks have tackled some of Crawford’s subjects (way too many of which have been reviewed here to listing), the closest to her integrated structural strategy is The Fees of Link by Nicholas Couldry and Ulises A. Mejias, which views our current technological reconfiguration as the beginnings of a new marriage among colonialism and capitalism. 

“Any sufficiently advanced engineering is indistinguishable from magic,” Arthur C. Clarke famously wrote. Pursuing Crawford, this appears to be a lot more like: “Any engineering that appears to be like magic is hiding anything.” So many darkish secrets lie in how the sausage is created. 

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