LOGISTICAL PROTOCOLS



Described by Anna Tsing as “supply chain capitalism”, the contemporary political economy finds its heart in logistics - the science of control and management of flows (Tsing, 2009). Logistical networks set complex protocols that determine everyday existence. Logistical protocols create new places for class conflict that are as material and important as the factory (Toscano, 2011). Current flows of goods and people scale up from local food delivery to eternal iterations of planetary distribution of products. The logic of such flows “carves the contours of contemporary capitalism” and maps its colonial influences: the ways logistical flows are facilitated and limited, the monstrous infrastructures that are created to serve them, racialised labour distribution they enable (Cowen, 2014; Chua, 2018; Mezzadra and Neilson, 2019). Logistical management creates immense structures that typically remain out of sight, hidden behind dots in the tracking apps, online status updates or products ready for consumption (Rossiter, 2016).



Through my video practice, I disentangle logistical infrastructures, to show material rules for neocolonial capitalist order. Cases I engage with let me discuss issues of planetary-scale through strategic heterogenous entry points, as one can see in “Machinic Infrastructures of Truth”, “Adversarial Infrastructure” and “Colonial Sediments”.

 




PROGRAMME



For Deptford Moving Image Festival I propose a 2.5-hour event:


30-minute performative lecture to overview the conceptual framework of the field of critical logistical studies. As a reference for the performative lectures I do, refer to the lecture “Feminism in Opposition”, based on my video-essay “Jewel”, for Boll Foundation. 

30-minute response from Laleh Khalili, leading researcher in critical logistical studies, whose book on logistics is being published with Verso in May, this year.

15 minutes Q&A and 15 minutes break

15 minutes “Adversarial Infrastructure” screening and 5 minutes break

15 minutes “Colonial Sediments” screening and 5 minutes break

20 minutes “Machinic Infrastructures of Truth.”

 


MACHINIC INFRASTRUCTURES
OF TRUTH



This project is a part of MIT, web-platform investigating algorithmic surveillance, commissioned by Garage Museum.

I investigate the contemporary ecosystem of workplace algorithmic surveillance deployed by Yandex, Russian Google-like IT monopoly. Yandex presents a unique case of an IT-monopoly that is involved in logistical networks through its multiple products, each being the part of the more extensive system, - taxi, food delivery, maps and algorithmic solutions for business logistics. Not having a competition with other monopolists in the field like Uber, the Russian branch of which Yandex bought in 2017, and far surpassing Google in all other markets, Yandex can create an enormous logistical network and solutions for its surveillance.


The majority of Yandex couriers and taxi drivers were anonymously reporting exhaustion, starvation, deaths due to overwork, and an impossibility for unionisation. Massive miscalculations of the time needed by the workers to cover certain distance make it impossible to rest or to comply with the delivery requirements as a whole, resulting in heavy cuts from the wages. In the first half of April, I will conduct five interviews with workers from the taxi and delivery Yandex services about their work experience, with particular attention towards their perception of space during work, algorithmic mistakes, and managerial concept of truth that these algorithms aim to guarantee.


So far, delivery workers have been talking publicly only under fake names, being concerned with their safety due to the total surveillance of Yandex. Therefore, most likely, these interviews are going to be presented anonymously, as modulated voices, leaving the space for the visual narrative to be structured as a 3D environment. This 3D environment will be produced as a flyover through the various photogrammetries of Moscow streets, that would be mentioned in the interviews. The 3D environment will change according to the narrative of interviews, giving the feeling of never-ending disorientation. Feeling of disorientation and vertigo is enormously important for the representation of cases, inflicted by colonial violence (Engelhardt & Shestakova, 2020). Overimposed with user-oriented app interface showing the corresponding route on the map, it will create the clash of front-end map, viewers are used to, against back-end embodied experience, always absent in logistical networks.

ADVERSARIAL INFRASTRUCTURE




I propose the term “Adversarial Infrastructure” to subvert the conventional understanding of bridges as connectors, objects of linkage that function in opposition to walls and borders, the emblematic tools of territorial disconnection and delimination. In 2016, Mark Zuckerberg, CEO of Facebook, proclaimed:

“Instead of walls, we can help people build bridges”.

This is not an invention of his PR team. Indeed, bridges exist wthin the social imagination as symbols of unity and peace, standing in contrast to the violent images of separation evoked by border walls.





Logistics is covering the violence it is inflicting more than efficiently by rendering itself invisible. Nevertheless, the problem is not only the reduction of flow to the destination point in the movement. When being analysed, logistics is too often understood as the “capacity to move goods” that aims for planetary flux and therefore exists in opposition to disruption. I present Adversarial Infrastructure as the logic of the contemporary mobility regimes⁠ — what Salter defines as a new form of logistics that does not guarantee the mobility of any actor but instead brings about its restriction (Salter 2013). It is a structure in which certain actors are rendered (im)mobile, and their capacity to function outside of the programmed pattern is “removed from the political realm and treated as either technical or economic questions” (Salter 2013, 9).


The term “adversarial” comes from its use in machine learning methods. Adversarial machine learning is based on the idea that algorithms can learn via competition. Generative adversarial networks (GANs) consist of two neural networks that are designed to be antagonistic to one another. Even though their functions are programmed to oppose each other, their entrapment in a looped contest strengthens the neural network as a whole.This same capacity defines Adversarial Infrastructure: while divergent functions may appear to create friction and technical problems for one another, when taken together they actually strengthen the infrastructure’s capacity to inflict harm.