Surveillance Rhetorics – Issue 2

“Lyft Atlanta” by danxoneil is licensed under CC by 2.0

News about ride-sharing services seem to be popping up wherever I go. I used an Uber for the first time this past fall when I traveled to Georgia for a conference (too far for me to drive). I found the experience interesting not just because of the drivers themselves (all very friendly) but in the way that the application and its algorithms are mediating the relationship between two people. Essentially, Uber and Lyft receive all the benefits of a decentralized, (under)regulated ride-sharing industry without any of the liability that comes with more traditional ride-hailing companies. As Javier Zarracina reports in his Recode article “Why Companies Like Lyft and Uber Are Going Public Without Having Profits” companies like Uber and Lyft are operating a loss but that this follows a general startup trend of “growth over profit.” Not that different from what I see Amazon accomplishing in its earlier days, the long game of growth and all the disruption it causes for institutions, companies, markets, etc. has the potential to yield massive profit…eventually. What sometimes gets lost in this race to the top are the everyday costs of the climb. It is not just that ride-sharing drivers front all of the operating costs (vehicles, insurance, gas) with no company benefits (no health insurance for instance), but also at stake is also the reshaping of cities and the priorities for its development. So what does this have to do with surveillance rhetorics specifically? Quite a lot. These examples demonstrate surveillance’s ties to power and the control of information in addition to how surveillance operates as an important tool for restructuring relationships between people, spaces, institutions, public policies, and urban development (rhetoric). For this newsletter, I tried to connect some threads together that cover the different interests affected by ride-sharing and its surveillance potentialities. The first academic article gets at the experiences of ride-sharing drivers and their contending with the services and their algorithms that they rely on to make a living. The second item is an episode of the show Elementary that hints at tensions between the old way of ride-hailing (cabbies) and new way of ride-sharing (drivers) but by episodes end turns into an interesting meditation on the potential dangers in our giving up so much information to these companies (especially when we consider what it might be used for). While it may seem a little more loosely connected to surveillance rhetorics, the last item for this newsletter examines the ways that ride-sharing companies shape our public infrastructures, services, and priorities in not necessarily the best ways; I see it as tracing the “further down the road effects” of what Shoshana Zuboff might call our surveillance capitalism moment.

Anderson, Donald Nathan. “Wheels in the Head: Ridesharing as Monitored Performance.” Surveillance & Society, vol. 14, no. 2, Sept. 2016, pp. 240–58. Crossref, doi:10.24908/ss.v14i2.6018.

In light of how fast information amasses and circulates, 2016 may seem not recent enough, but Anderson’s article speaks to issues that persist with ride-sharing services to this day. Anderson works through the soft control in ride-sharing apps over the behavior of their drivers. Building from Galloway and Wark’s allegorithm – “the productive co-deployment of a socially relevant allegorical script and a software-mediated algorithm” – Anderson examines the relationships facilitated for ride-share drivers and customers through the ride-sharing applications that perform calculations for ratings, fares, and requests. Said apps control driver behavior along three metrics: control of work and pay [algorithmically decided fares]; control of information [limiting what information drivers have access to and guiding them towards algorithmically determined hotspots of activity to maximize profit]; and monitoring performance [conditioning driver vigilance through unclear rating systems that dictate their ability to work]  (244). Anderson concludes that these soft cabs are an example of oligoptica or the assemblage of surveillance apparatuses (algorithms, sensors, etc) that are meant not to discipline in the Foucauldian sense but rather to control in the Deleuzian sense; not the interiorization of social rules/expectations but rather monitoring outcomes (255-256). What I find most interesting about the ride-sharing service algorithms in this sense is that the algorithm is the mediator between the driver and the rider, rather than an institution/business. The algorithm has a specific understanding of behaviors the driver should perform but also what a rider can/should expect of the experience and it nudges them both towards these respective roles (a sort of social sorting in its own right, but arguably more at the driver’s expense). I also think its interesting that drivers have formed communities in online forums where they support each other in figuring out how to make the best of the allegorithm, the system they can never completely understand but have to understand enough to make a strategy that makes it worth their time. That being said, I will think twice about rating any driver anything less than a five-star considering how much decimal points matter in determining how whether they can even continue on the service (let alone to eek a living out from it).

Elementary: S03E18 – The View From Olympus (viewed on Amazon Video)

I have not watched a lot of CBS’s Elementary but this particular episode was recommended to me because it resonated with some of the surveillance concepts I talked to a professor about. The clip linked above opens with the murder of a ride-sharing driver and Detective Bell’s initial theory that it might have been a disgruntled cab driver because “there have been a lot tension between the cabbies and the ridesharing guys the last few months” (5:00). Provided what we have explored earlier, this is not an inaccurate depiction of some of the underlying tensions with ridesharing service disruptions of people’s livelihood. Where the episode ends up reveals yet another layer to this puzzle. Sherlock takes a closer look at Zooss (the fictional version of Uber/Lyft for this world) and uncovers a blackmailing conspiracy at the heart of the service. In a not completely implausible read of the potentials with the data-harvesting and mapping required for ride-sharing services to operate, Sherlock points out that Zooss is the “most sophisticated surveillance and tracking device known to man” (23:39). The episode goes on to show how even only knowing “where we are picked up” and “where we go” actually gives plenty of information to deduce what people are more than likely doing and what their routines are, or in the very least enough for a blackmailer to work with in finding people with “things to hide” (24:00-25:39). What is important is our patterns and what can be deduced from them (33:15-35:45). Again, while the plot is certainly far-fetched, the technology representation is not that far off and should make us reconsider how important our seemingy use-less data is and what, when taken in aggregate, it reveals about us. There is a larger discussion to be had about the trust we place in these companies, especially when we understand what is possible with the data they collect about us with very little insight (from us) or oversight (from our institutions).

“Uber and the Ongoing Erasure of Public Life” – Nikil Saval (The New Yorker)

Published not too long before ride-sharing services went public, Saval’s piece from The New Yorker offers an interesting perspective on the longer term implications that ride-sharing services have (and will continue to have) on our infrastructures and urban development. Rather poignant, Saval explains that the most noticeable effect of ride-sharing services (when we look beyond the screen) is how they “are sapping transit ridership and clogging streets.” Part of what has allowed Uber and Lyft to edge out their taxicab and public transportation competition is that they operate in an unregulated space that their aforementioned competition still adheres too (is this cheating?). As might be argued of many social media platforms, Saval notes how Uber/Lyft remove the “friction” of customers’ experience hailing rides, but the larger question remains as to at what cost? What is included in that friction that ensures accountability? What would ensure that the growth of these companies are for the benefit of the public rather than about gutting our transportation infrastructure to then massively raise prices once they are the only show in town? Rather than reducing the amount of cars on the road, ride-sharing has simply increased the amount of idle vehicle congestion. Essentially, Uber/Lyft represent more than a disruption or an additional layer of surveillance and tracking that shapes our experience navigating public spaces, its disruptions make much more apparent (for the time being) the flows of information and transportation networks we rely on and the institutions, public interests, and power dynamics built into the fabric of these networks. This is the “friction” Uber/Lyft are shaking up (sometimes necessarily so) but without providing a suitable replacement. The danger is that we may just find ourselves in a less efficient, more costly transportation situation than we have currently, not to mention the prospects for our data (and our patterns of behavior that may be discerned from it) if Uber/Lyft have to shake up their business models later down the road.