Global judgements and ideas.
Phoebe V. Moore, Kendra Briken, Frank Engster.
Read the introduction for our forthcoming Special Issue in Capital & Class, “Machines & Measure”, edited by Phoebe V. Moore, Kendra Briken, Frank Engster.
This Special Issue, entitled ‘Machines & Measure’, is largely the dissemination from a workshop held at University of Leicester School of Business, organised by editor Phoebe V. Moore, for the Conference for Socialist Economists (CSE) South Group in February 2018, which was hosted by the University of Leicester School of Business, Centre for Philosophy and Political Economy (CPPE). The collection provides a carefully selected, representative collection of articles and essays which address the questions and disturbances that drove the event’s concept as articulated in the event description. How are machines being used in contemporary capitalism to perpetuate control and to intensify power relations at work? Theorising how this occurs through discussions about the physical machine, the calculation machine and the social machine, the workshop was designed to re-visit questions about how quantification and measure, both human and machinic, become entangled in the social. Papers look at how the incorporation and absorption of workers as appendages within the machine are Marx identified, where artificial intelligence (AI) and the platform economy dominate today’s discussions in digitalised work research. Stemming from Marxist critical theory, questions of money, time and space are also revisited in the Special Issues articles, as well as less debated concepts in rhythmanalysis, alongside a revival of historically frequently discussed issues such as activities on the shopfloor, where a whole range of semi-automated and fully automated methods to manage work through numeration without, necessarily, remuneration, continue. Articles ask the most important questions today and begin to identify possible solutions from a self-consciously Marxist perspective.
This paper analyses crowdwork platforms where various forms of digital labour are outsourced to digital workers across the globe. The labour of these workers is, amongst other things, a crucial component in the production, development and support of artificial intelligence. Crowdwork platforms are an extreme example of new forms of automated measurement, management and control of labour allowing, in turn, for the creation of hyper-flexible and highly scalable workforces. Particularly on so-called micro-task platforms work is characterised by decomposition, standardisation, automated management and surveillance, as well as algorithmically organised cooperation between a great number of workers. Analysing these platforms as a paradigmatic example of an emerging digital Taylorism, the paper goes on to argue that this allows the platforms to assemble a deeply heterogeneous set of workers while bypassing the need to spatially and subjectively homogenise them. These platforms create a global on-demand workforce, working in their private homes or internet cafes. As a result, crowdwork taps into labour pools hitherto almost inaccessible to wage labour. The second part of the paper investigates this tendency by looking at two sets of workers: women shouldering care responsibilities who now can work on crowdwork platforms while performing domestic labour as well as digital workers in the Global South. While there are clear specifics of digital crowdwork, it is also an expression of broader transformations within the world of work, concerning, for example, new forms of algorithmic management just as the return of very old forms of exploitation such as the piece wage.
This paper discusses new technologies in regards to their potential to capture workers’ situated knowledge. Machines are said to substitute but also to contribute to the labour process in collaboration with human skill sets. ‘Industry 4.0’ became the policy-wide shorthand to describe the new quality of real time interconnectedness and feedback loops, known as cyber-physical systems (CPS) within industry and engineering sciences. Data flows generated in these systems are used to continuously improve work processes by extracting information down to the very micro level of neuroergonomics. In this process, workers’ interactions with the system are extracted, feed back and processed for future use and improvement. The paper argues that in addition to the potential for extraction of new (bodily) knowledge, shifting skill use, and the potential for new forms of control, new technologies contain the potential to extract situated knowledge owned by the worker and crucial for resistance and collective struggles.
Marco Briziarelli and Emi Armano
In this paper we revive Lefebvre’s perspective on the production of space contextualized within the recent debate on digital capitalism, and we explore the antagonism between capital and labor from a distinctive spatial and connective perspective: by examining the tension between the production of digital abstract space in the context of machines and computational automation, and the powerful push backs of embodied labor struggles of gig-economy workers advancing alternative connective strategies.
Digital Connectivity creates a sphere that facilitates the post-Fordist subsumption of lived into abstract space, as well as use value into exchange value by re-organizing space in delivery logistical venues and by quantifying, measuring and automatizing logistical tasks, thus closing/interlocking social relations around capitalist expansion objectives; on the other hand, the inherent relational-connective logic of (digital) mediation—i.e. the machinic condition of co-production of value— also opens up to alternative directions.
We concentrate on the role played by workers in mediating principles of alternative-connectivity against the general tendency of casualization of work in the gig/digital economy. Based on the present analysis, we point to the theoretical and political challenge to dialectically identify when and how the very machinic combination of digital technology and human agency that facilitates capital accumulation also provides conditions for re-appropriation and self-determination.
The aim of the text is to clarify why machines are economically productive only in capitalism and therefore in our society are capitalistic machines. They are capitalist not only because they increase the productive power of the capitalist valorisation, but this valorisation first of all is producing these machines, or at least it produces their productivity and hence ‘the machinic’ of machines. To understand this production of the machinic, we must understand them, as, for example, Heidegger, Simondon or Deleuze and Guattari have shown, from their context: from their non-technical essence, from their connection with other machines and from the social essence of the machinic. But in this context, first of all and in the last instance, we have to understand with Marx as their entanglement with the capitalist valorisation. This can be shown for three different types of machines: the physical machine, the calculation machine and the social machine: money. What all three have in common and almost defines them as machines is that all three naturalise relations by quantifying them. The classical physical machine quantifies the relation of nature, the calculation machine quantifies information and meaning, and the money machine quantifies the relations of our society. I will concentrate on the physical, and the money machine only. The technique to quantify is for both the same: measurement. This quantification and naturalisation by measurement is why both are – although or especially because they are opposed types of machines – interfaces to the capitalist valorisation process, and in this functioning as interfaces, we have to search their non-technical essence.
Frank Engster and Phoebe V Moore
Artificial intelligence (AI) is being touted as a new wave of machinic processing and productive potential. Building on concepts starting with the invention of the term AI in the 1950s, now, machines can supposedly not only see, hear, and think, but also solve problems and learn, and in this way, it seems that actually there is a new form of humiliation for humans. This article starts with a historical overview of the forerunners of AI, where ideas of how intelligence can be formulated according to philosophers and social theorists begin to enter the work sphere and are inextricably linked to capitalist production. However, there always already has been an AI in power in on the one hand, technical machines and the social machine money; and on the other, humans; making both sides (machines and humans), an interface of their mutual capitalist socialisation. The question this piece addresses is, then, what kind of capitalist socialisation will the actual forms of AI bring?
Ten years up to the present, Uber Technologies has become a world leader in the market for transportation via private cars. Using machine learning and artificial intelligence (Kadous, 2017), the company is revolutionizing human tracking and geolocating technologies. Moreover, the application resorts to a third-party evaluation — a 5 stars rating system — to further examine the in-car experience and discipline drivers accordingly (Meredith & Sorkin, 2018). This article aims at contributing to the research on the algorithmically intensified control of — drivers in the case of Uber. It argues that the application’s algorithmic apparatus is playing a threefold role; first, it engineers the overall labour process through a set of decisions that dictate the organizational setup. Second, it assures the intensification of production which allows an organization such as Uber to simultaneously forecast demand and supply in 600+ cities and ensure 99.99% availability (Bell, 2017). And third, the algorithmic apparatus allows for a continuous documentation and categorization of actions conducted by all active subjects within the realm of the application. This system of intelligent machines is, in fact, inducing a state of “permanent visibility that assures the automatic functioning of power” (Foucault, 1977) while inducing a sort of digital incarceration of drivers behind the wheel in their own cars. The Uber surveillance apparatus as interpreted in this article represents an extension of a wall-less Panopticon that we define as the Algopticon.
Digitalisation has two very different effects on work. On the one hand, it leads to a re-Taylorisation of work, de-qualification and a loss of workers autonomy. On the other hand, digitalisation of work leads to new forms of indirect control and algorithmic control that can be used to manage and instrumentalise the supposed autonomy of workers to actually enable an unequal and exploitative labour process. This article discusses the questions of heteronomy related to the digitalisation of work, presents centrals aspects of new forms of control (direct, indirect and algorithmic) and explains why formalisation, data centred decision making and flexible structures are used to control the labour process and improve heteronomy of work.
Phoebe V Moore
‘The mirror for (artificial) intelligence: In whose reflection?’ sets out the parameters for caution in considering as-yet relatively un-debated issues in artificial intelligence (AI) research, which is the concept itself of ‘intelligence’. After the failed AI ‘winters’ ending in the late 1990s, a new AI summer commences. What is still missing is a careful consideration of the historical significance of the idea, that is to say, the weighting that has been placed on particular aspects of consciousness and surrounding seemingly human-like workplace behaviour, which takes increasing significance given the renewed interest in machinic intelligence emerging. From the British empirical philosophers to cyberneticists’ revelations, this article argues that a series of machinic and technological invention and related early experiments demonstrate not only the origins of a process of normalization of what are considered intelligent behaviours, via both human and machinic intelligence, but also facilitate and enable the integration of calculation and performance measuring machines into everyday work and life. Today, ideas of autonomous machinic intelligence, seen in the ways AI-augmented tools and applications in human resources, robotics, and gig work are incorporated into workplaces, facilitate workplace relations via machinic intelligent behaviours, that are explicitly assistive, prescriptive, descriptive, collaborative, predictive and affective. The question is, given these now autonomous forms of intelligence attributed to machines, who/what is looking in the mirror at whose/which reflection?
Frederick Harry Pitts, Eleanor Jean and Yas Clarke
Today there is a proliferation of wearable and app-based technologies for self-quantification and self-tracking. This paper explores the potential of an Open Marxist reading of Henri Lefebvre’s rhythmanalysis to understand data as an appearance assumed by the quantitative abstraction of everyday life, which negates a qualitative disjuncture between different natural and social rhythms – specifically those between embodied circadian and biological rhythms and the rhythms of work and organisations. It takes as a case study a piece of performance research investigating the methodological and practical potential of quantified-self technologies to tell us about the world of work and how it sits within life as a whole. The prototype performance research method developed in the case study reconnects the body to its forms of abstraction in a digital age by means of the collection, interpretation and sonification of data using wearable tech, mobile apps, synthesised music and modes of visual communication. Quantitative data was selectively ‘sonified’ with synthesisers and drum machines to produce a forty-minute electronic symphony performed to a public audience. The paper theorises the project as a ‘negative dialectical’ intervention reconnecting quantitative data with the qualitative experience it abstracts from, exploring the potential for these technologies to be used as tools to recover the embodied social subject from its abstraction in data. Specifically, we explore how the rhythmanalytical method works in and against the reduction of life-time to labour-time by situating labour within the embodied time of life as a whole. We close by considering the capacity of wearable technologies to be repurposed by workers in constructing new forms of measurement around which to organise and bargain.
Digital capitalism produces knowledge as a commodity. Machines and
algorithms manage the value chain, concrete human labour is less and
less necessary – allegedly. According to Karl Marx, the system would be
on the brink of collapse. Is that correct?