Reading Four

Anatomy of an AI System by Kate Crawford and Vladan Joler is a super ambitious project detailing the behind-the-curtain processes that go into ever-popular home assistant AI systems that are branded and designed particularly to mask this complexity. I found that distinction to be very interesting - the Amazon echo is a simple and sleek black cylinder with no buttons marketed to be a hassle-free way to make your life a bit more convenient. Meanwhile, the process to excavate and assemble the raw materials it requires, train it through a complex system of human labor and data, and manage the end of its life cycle could not be more "out of sight and out of mind" for its users.

I really valued the perspective of this piece, discussing aspects of our current cultural technological infrastructure that are incredibly costly but are dis-aggregated from the consciousness of the vast majority of that infrastructure's users. Shedding more light on the natural resource strain that is caused by constant phone upgrades is an aspect I'll admit I haven't heard much about or considered, but one that they rightly focus on as the "begin and end" of the process of the Amazon echo dot (as a microcosm of the countless other devices and examples that produce a strain on the finite resources of our plant). This line in particular has stuck with me:

Each object in the extended network of an AI system, from network routers to batteries to microphones, is built using elements that required billions of years to be produced. Looking from the perspective of deep time, we are extracting Earth’s history to serve a split second of technological time, in order to build devices than are often designed to be used for no more than a few years.

Also ever-important is the often unconsidered human cost of the technology supply chain, which includes numerous examples of exploitation and unsafe working conditions. I found the fractal imagery an effective one to portray this message - it is a nice corollary to see how individual triangles form part of a whole. Each triangle is itself incredibly small, but is an integral part of the foundation that enables the creation of this behemoth of a triangle, with a company or individual sitting at the top and reaping most of the rewards.

The authors' perspective on how users of technology are commodified and made both product and worker is also a salient one, one that I think is overlooked by the vast majority of the population. They point out that "the Echo user is simultaneously a consumer, a resource, a worker, and a product", whose interactions with the device are being used to train the AI system and make it that much more sophisticated. In this vein I read the New York Times piece Artificial Intelligence, With Help From the Humans. It references to the example the authors discuss in their reading, the 'Mechanical Turk', which appeared to be a machine which reached an unprecedented level of sophistication at chess, but was actually controlled by an unseen human individual. The way this strategy has been applied to contemporary technology is both ingeniously impressive and frighteningly distressing. We think of feedback loops with AI as humans asking computers a question and getting a result back, while this perspective flips that relationship on its head. The computer attempts to ask a question that a human is much better at answering, using that feedback loop to inform or validate AI logic. Mechanical Turk has used crowdsourcing to find people that can complete simple, almost mindless asks that earn them some easy income (albeit in tiny increments for each task), but provide invaluable insight to AI logic and algorithms that would struggle mightily in completing these tasks without human input.  While Mturk pays individuals micropayments for this service, implementing CAPTCHAs for human verification have effectively unlocked the entire internet userbase as free labor for AI systems in this same way.

From a pure data visualization perspective, I have great respect and admiration for a project that attempts to model something so complex and over-arching like this lifecycle of AI that includes "the history of human knowledge and capacity" only as one aspect of the full visualization, which they put "at the bottom of the map". I think the visualization is largely successful in communicating the complexity of this system, but like we mentioned with the node diagrams, shooting for exhaustiveness in a chart like this can often be a bit hard to ensure and hard to comprehend. I would say that I wouldn't get the same value from the PDF viz without reading the authors' text that goes with it.

Show Comments