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

“Request forgiveness, not permission” has extended been a guiding principle in Silicon Valley. There is no technological discipline in which this principle has been a lot more practiced than the machine finding out in modern day AI, which is dependent for its existence on large databases, just about all of which are scraped, copied, borrowed, begged, or stolen from the large piles of data we all emit everyday, knowingly or not. But this data is hardly ever rigorously sourced with the subjects’ permission.  

“Since we can,” two sociologists explain to Kate Crawford in Atlas of AI: Power, Politics, and the Planetary Expenses of Artificial Intelligence, by way of acknowledging that their academic institutions are no diverse from know-how organizations or federal government businesses in with regards to any data they obtain as theirs for the getting to teach and examination algorithms. Pictures come to be infrastructure. This is how machine finding out is manufactured. 

Everyone wants to communicate about what AI is very good or dangerous for — determining facial pictures, deciphering speech commands, driving autos (not nonetheless!). Several want to pour ethics over present day AI, as if creating rules could alter the armed forces funding that has defined its fundamental nature. Few want to discuss AI’s legitimate expenses. Kate Crawford, a senior researcher at Microsoft and a research professor at the University of Southern California, is the exception. 

In Atlas of AI, Crawford starts by deconstructing the well known rivalry that ‘data is the new oil’. Normally, that leads people to communicate about data’s financial worth, but Crawford focuses on the point that both equally are extractive systems. Extraction is mining (as in ‘data mining’ or oil wells), and in which mining goes, so follow environmental hurt, human exploitation, and profound modern society-extensive penalties.  

Crawford underlines this stage by heading to Silver Peak, Nevada, to pay a visit to the only working lithium mine in the US. Lithium is, of training course, a essential component in battery packs for every little thing from smartphones to Teslas. Crawford follows this up by thinking of the widening implications of extraction for labour, the sources of data, classification algorithms, and the country-condition behaviour it all underpins, ending up with the power buildings enabled by AI-as-we-know-it. This way lies Project Maven and ‘signature strikes’ in which, as previous CIA and NSA director Michael Hayden admitted, metadata kills people. 

Snake oil

Nevertheless some of this is patently wrong. Crawford traces again the picture datasets on which the latest disturbing snake oil — emotion recognition — is based mostly, and finds they ended up designed from posed photographs in which the subjects ended up instructed to supply exaggerated examples of emotional reactions. In this situation, ‘AI’ is manufactured all the way down. Is there, as Tarleton Gillespie requested about Twitter tendencies, any real human reflection there? 

Whilst other know-how guides have tackled some of Crawford’s subjects (as well several of which have been reviewed here to checklist), the closest to her integrated structural method is The Expenses of Relationship by Nicholas Couldry and Ulises A. Mejias, which sights our present technological reconfiguration as the beginnings of a new marriage between colonialism and capitalism. 

“Any adequately state-of-the-art know-how is indistinguishable from magic,” Arthur C. Clarke famously wrote. Adhering to Crawford, this seems a lot more like: “Any know-how that seems like magic is hiding something.” So several dark strategies lie in how the sausage is manufactured. 

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