Kate Crawford’s new publication applies Life Cycle Analysis to explore what Artificial Intelligence ‘is’
The definition of technology remains elusive, and has been prone to large shifts through the decades alongside modernization. A generally time-independent and agreed upon definition states that technology is a means to utilize systems, machines or devices to leverage scientific knowledge for practical human applications. By this definition, Artificial Intelligence (AI) is classified as a technology, which uses computational means and systems to better understand and make sense of data. So we can view AI as a technology which is used for transporting and converting data for human consumption and comprehension in the same way that we use an automobile to transport us from one point to another, or a generator to convert mechanical or chemical energy into electricity. When I came across the title of Kate Crawford’s Atlas of AI, my initial inclination was to expect another book focusing on three recurring themes: (1) how AI can be used to transform new industries, (2) how it will change our lives, and (3) where the newly generated global centers of power will reside. Upon reading the book - and much to my delight - it was about none of these messages.
As an evolutionary complexity scientist, I was thrilled to see Steven Jay Gould’s work referenced when describing errors in systematic classifications — an issue that sits at the cornerstone of problems related to AI bias. As a geospatial/AI/data scientist, I was also immediately hooked by the ‘Atlas’ reference in the title (for more, see Atlas Research Innovations, and FutureMap). An atlas, by definition a collection of maps, is supposed to provide the reader with a visual frame of reference regarding spatial relationships. And the Atlas of AI does just this. Dr. Crawford very effectively describes the AI world through the spatial and temporal relationships that we would expect when perusing the pages of an atlas. Instead of looking forward and trying to forecast where society will most effectively utilize AI applications in the decades ahead, Crawford takes a Life Cycle Analysis deep-dive into the material side - the nuts and bolts - of AI. Beginning with Chapter 1, aptly titled ‘Earth’, she describes what AI actually is, and not what it does. It becomes apparent that the cloud analogy has allowed us, for decades, to distance ourselves from internalizing the costs associated with the technologies that sit at our fingertips by allowing us to use a metaphor which conjures up an image of something fluid and far away. Unlike the visualization of ‘the cloud’, it is easy to visualize the negative externalities associated with the burning of fossil fuels, the loss of biodiversity, or polluting the oceans. But technologies which live in the cloud provide an abstract mental barrier which makes it more difficult to distinguish between costs and benefits. Simply by highlighting this conceptual misalignment between the perceived and reality of digital, non-physical externalities, Crawford creates a new dimension and set of tools through which are able to create such a cost-benefit analysis.
A further question related to digital, non-physical goods that Crawford specifies is: What is AI actually composed of? In short, AI is a technology like any other, comprised of both commodity and specialty materials, each carrying their own portfolio of environmental and human labor costs, built upon societal inequities, and (largely) serving the privileged. From the first few sentences in Earth to concluding with the final chapter describing how the world’s billionaires are hedging society’s technological bets in Space, Crawford literally brings this notion of ‘somewhere else’ down to our planet, and does so in a convincing and thought provoking manner. So while we rightly continue to applaud AI applications which support the rise of renewable energy utilization, the proliferation of electric and self-driving vehicles, and systems that minimize resource extraction and use, all of these enablers powered by AI have their roots firmly planted in soil enriched with massive computational architectures. These architectures which, in the process, are using vast quantities of rare materials and relying on massive energy requirements.
Upon completing the book, which I look forward to re-reading, my belief remains unchanged and hardened that as applied researchers our goals should remain looking for ways to effectively use AI to help society cope with a growing list of globally interconnected problems. However, we should also try to learn from history and not solve one class of problems, only to create another. Dr Crawford’s Atlas serves as a guide which should influence our thinking in this space for the decades ahead.
(thanks to Sinead O’Sullivan for edits & suggestions)