I was especially struck by Wendy Chun’s purposeful use of the term harvesting when referring to the way in which Cambridge Analytica extracted data on Americans via Facebook in her chapter “Correlating Eugenics” (2021). This evocative language provokes questions rooted not just in consumerism, but in ethics and their intersections with “the digital.” In what ways is our information stolen and in what ways do we offer ourselves up in a series of 1s and 0s by willingly contributing to these data networks—planting the seeds and sowing the lands, to be harvested.
In another class I am taking this semester, we discussed interactive media, specifically the eroding boundaries between interactive cinema and gaming. Where this discussion engages with Chun’s theorizations is how the label of play can act as a façade for this “harvesting” (38-39).
In 2018, Netflix developed an interactive film experiment titled Black Mirror: Bandersnatch (Slade). In this film-game hybrid, the viewer is asked to make choices which, ultimately, affect what type of ending occurs at the end of the film. While being marketed as a mode of entertainment, these choices also act as prime sources of information mining on both commercial and social levels. The first choice the participant is asked to make is an issue of brand preference or brand loyalty: you (taking on the role of the teenage boy protagonist) are asked to decide what to eat for breakfast: Frosties or Sugar Puffs. These consumer data sets later evolve to much more dramatic and existential questions. In a couple instances you are forced to choose whether to take the life of another—literally deciding who lives and who dies, under the guise of art.
This obfuscation between data, choice and social prediction also has resonance in context with the first chapter of John Cheney-Lipold’s We Are Data: Algorithms and the Making of our Digital Selves (2017), in which he explains how data collection is used to decide which lives are valued and who is expendable in the war on terror, or as the author calls it, citing journalist Tom Engelhardt, “the war of terror” (39).
This leaves us with several unanswered and somewhat troubling questions such as where is this data going? How is it stored? What kind of conclusions or manipulation tactics can be gleaned from information that is not only stolen—or quasi legally bought, as is the case with Cambridge Analytica—bought voluntarily provided by users themselves?
References:
Cheney-Lipold. “Categorization: Making Data Useful.” We Are Data: Algorithms and the Making of our Digital Selves. New York University Press, 2017.
Chun, Wendy. “Correlating Eugenics.” Discriminating Data: Correlation, Neighborhoods, and the New Politics of Recognition. MIT Press, 2021.
Slade, David, director. Black Mirror: Bandersnatch. Netflix, 2018.
Dalia Hatalova Blog post #4/15
ReplyDeleteHi Maggie, I was intrigued by your pointing the discussion in the direction of expendability and storage. When speaking of data, the voracious appetite of algorithms to increasingly store more and more data is countered by the problems of where to keep it and the subsequent environmental consequences. Data provides a crucial instance of the digital leaving a physical footprint, as Kate Crawford observes, constructing a new form of extractivism. However, I believe this new form of extraction centered on data collection simultaneously shapes and influences other, broader issues in the economic system. By prioritizing profit, physical losses become sanctified by the overall accumulation of capital – only possible to be administered and maintained due to algorithmic possibilities for calculations. While Amazon may sell Kindles at cost or lower in order to maintain customers and Amazon Echo may similarly be purchased at low costs because of its value in garnering data (as mentioned in class), these calculated practices for gaining capital bleed off into the world of physical commerce. As Crawford remarks, Amazon is unique in lacking transparency about its practices and environmental footprint. She writes, "among the global cloud providers, only AWS still refuses to make public basic details on the energy performance and environmental impact associated with its operations." Perhaps, Amazon's structure, not only in data extraction but in spending resources merely to create an overall profit – measurable through algorithmic calculations – prove too unethical to be released. Amazon's practices of returns, for example, reveal that not all of the items returned are actually resold, despite being in resellable condition. Instead, they contribute to the accumulation of trash on the planet without ever having had any use. Business Insider's article by Isobel Asher Hamilton explains, "This means if a product isn't selling on Amazon, the cheapest option is to pay Amazon to get rid of it" (https://www.businessinsider.com/amazon-throws-away-new-products-waste-third-party-sellers-profitable-2021-6). Crawford delineates how
"standardized cargo containers allowed the explosion of modern shipping industry, which made it possible to model the planet as a massive, single factory" and its environmental impact whereby "shipping boats produce 3.1% of global yearly CO2 emissions." Hence, to the environmental damage caused by the accumulation of trash, we must add the expendable energy of production and transportation before the 'useless' product is abandoned in the junkyard. Additionally, the shipping boat example is likewise akin to that of data-driven commerce, as both enable a globalized view of markets. With data sets, a company such as Amazon is able to take in the 'big picture,' despite the mass destruction of the environment felt at ground level, were abandoning a product may be the more profitable option because of the scale of sales. With a small shop owner, such data would be both unavailable and unidentifiable without the substantial data available that Amazon has as a global platform. These issues point to the way that data not only creates a new market but drastically alters approaches to more traditional forms of commerce.