The mirror for (artificial) intelligence in capitalism

Introduction

Norbert Wiener (1948[1961]) cogently noted in 1948 in Cybernetics: Or Control and Communication in the Animal and the Machine that ‘the thought of every age is its technique’ – in other words, that there are continuous entanglements between human thought and machinic invention, which is the case in the history of ideas about intelligence. The ways that technologies and machines are incorporated into societies, and humans’ relationships with machines, reveal ideas about what types or features of intelligence are considered to be valid for humans and, increasingly today, for machines. As artificial intelligence (AI) becomes increasingly debated and is now being introduced into workplaces as a way to make human resource (HR) and work design decisions, it is important to discuss what is meant by intelligence, in what context and think about what is, overall, at stake.

Indeed, the development of thinking around intelligence and what it might be does not stand in isolation of social circumstances or political economy. The authors of Stanford University’s One Hundred Year Study of Artificial Intelligence claim that there is a ‘lack of a precise, universally accepted definition of AI’, which has ‘helped the field to grow, blossom and advance at an ever-accelerating pace’ (cited in Turner 2019). What these authors overlook is that AI is not only about intelligence, but it relies on intelligent behaviour: as cited below, the person who is credited with inventing the very term AI described ‘making a machine behave in ways that would be called intelligent if a human were so behaving’ (McCarthy et al. 1955). There is a wealth of research in philosophy, sociology and psychology about ‘what is intelligence’, and the thinkers who influenced early AI researchers, even if not precisely referenced, lend purpose to ‘intelligent’ behaviour. The problem with the history of ideas that led up to depictions of AI is that thinkers around the concept of intelligence (as an idea) overlook the material conditions that could or would be perpetuated by virtue of such intentional, and not inevitable, intelligent behaviours (e.g. within capitalism, competition, class oppression, division of labour) – that is, except Karl Marx, who focussed on material conditions in his analysis and was, of course, a historical materialist.

This piece expands on the historical episodes outlined in the article by Engster and Moore in the current Special Issue, to develop the historical materialist critique of the history of ideas leading up to and during the eras of AI, but also as a way to critique the contemporary moment where machines are ascribed autonomous intelligence. Specifically, the history of the ideational manufacturing of human intelligence demonstrates a pattern of interest in calculation and computation, intelligent human and machinic behaviours that are, not surprisingly, ideologically aligned with capitalism. The simultaneous series of machinic and technological invention and related experiments shows how machines not only facilitate the processes of normalisation of what is considered intelligent behaviours, via both human and machinic intelligence, but also facilitate and enable the integration of capitalism into everyday work and life. Intelligent behaviours are identified as the capacity for quantification and measure and are limited to aspects of thinking and reasoning that can provide solutions to for example, obstacles in production and extraction of surplus value, based on the specific postulations and assumptions highlighted in this short text.

So, this piece builds on my arguments that perceptions of intelligence, or ‘behaviour that is considered intelligent’ as stated by McCarthy in his original description of what AI is/could be in 1955, over time reflect the contextual political economic and social order, the inventions of specific machines and what are considered to be machinic capabilities for intelligence. Today, ideas of autonomous machinic intelligence, seen in the ways AI is incorporated into workplaces outlined in the sections below, facilitate workplace relations via intelligent behaviours that are 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?

The intelligence of humans, by humans

It is impossible to cover all ontological and epistemological debates leading up to and during the phases of AI research, which started, in name, in 1955. But some important moments in the development of human reason and thinking stand out and are outlined in Engster and Moore (current issue). The present piece, in that light, outlines how scholars’ ideas were further contemplated by the school of cybernetics and the AI communities of research, where intelligence of both machines and humans are depicted, which eventually demonstrate a lack of clarity about which object in the discussion is in fact the mirror: the machine or the human? Characteristics of intelligence that humans have ascribed to humans, and more current forms of autonomous intelligence ascribed to machines, fit an overarching ideology of capitalism, where quantification and abstraction are the dominant modus operandi.

Intelligence as calculation parcels

In British empirical philosophy, the mind itself was seen to be composed of ideas. In the chapter ‘Of reason and science’ of Leviathan, Hobbes (1651) muses,

When a man Reasoneth, hee does nothing els but conceive a summe totall, from Addition of parcels; or conceive a Remainder, from Substraction of one summe from another: which (if it be done by Words,) is conceiving of the consequence of the names of all the parts, to the name of the whole; or from the names of the whole and one part, to the name of the other part. And though in some things, (as in numbers,) besides Adding and Substracting, men name other operations, as Multiplying and Dividing; yet they are the same; for Multiplication, is but Addition together of things equall; and Division, but Substracting of one thing, as often as we can . . .

Out of all which we may define, (that is to say determine,) what that is, which is meant by this word Reason, when wee reckon it amongst the Faculties of the mind. For Reason, in this sense, is nothing but Reckoning (that is, Adding and Substracting) of the Consequences of generall names agreed upon, for the Marking and Signifying of our thoughts; I say Marking them, when we reckon by our selves; and Signifying, when we demonstrate, or approve our reckonings to other men.

So, humans’ capacity for reason, which animals were not expected to have, is a process whereby we simply carve the world into symbolic units and use sums to make decisions, informing intention. Man can consider the consequences of our actions and make theories and aphorisms, reasoning and reckoning ‘not only in number, but in all other things whereof one may be added unto or subtracted from one another’ (Hobbes, 1651). Hobbes’ form of human intelligence is portrayed in terms of calculative processes.

Another major British empiricist, John Locke, held that the ideas that make up the human mind exist in a wholly passive way, where, on the basis of sensory contact with the outside world, they pull themselves into bundles of similarity, borders, or cause and effect. This is, interestingly, a process that is similar to the ways that neural networks much later were expected to behave. Later, Ivan Pavlov’s studies of dog responses to desired objects such as food were based on patterns of action. Locke had looked at ideas but not patterns. But bodies respond to stimulus, Pavlov noted, not only by generating idea clusters but also by actual empirical responses to repetitive stimulus. So, intelligent responses occur when the mind and body are faced with repeated exposure to an object or stimulus, again, an interesting predecessor to thinking about machine learning, where machines are expected to take note of and respond to patterns across data, where the data provide relevant stimulus.

The intelligence of machines, by humans

A couple of decades after scientific management fell out of fashion, prominent scientists sought, at the origins of the field of research called AI by McCarthy at Dartmouth College in 1955, to find ways to make machines directly behave like humans, intelligently. The early phase of AI research was committed to seeing ongoing human input into the processes of machinic intelligence formation and manifestation. Today, AI researchers have begun to focus on creating autonomous machines, where machines are expected to think for themselves; make decisions and choices; and are capable, even, of affective responses. This is the phase where General AI is considered imminent. But the early days of research were focussed on explicit attempts to make machines behave like humans, where the funding application to the Rockefeller Foundation for the summer events designed to create intelligent machines states that

. . . the artificial intelligence problem is taken to be that of making a machine behave in ways that would be called intelligent if a human were so behaving.

This funding application also indicates that

In a single sentence the problem is: how can I make a machine which will exhibit originality in its solution of problems? (McCarthy et al. 1955)

Text within the application already demonstrates that the original AI researchers believed that they understood, inherently, what intelligence is. However, humans behave in a myriad of ways, so it might have been a good idea to think about and define not only machinic behaviours, but human (intelligent) behaviours simultaneously, during these summer workshops on the American East Coast. Interestingly, psychologists were not consulted in these early phases on the ideas around intelligence and being. Intelligence as an idea was often, just assumed, by the small group of male intellectuals who are responsible for the origins of what they labelled ‘AI’ research and execution. It is not that psychologists are the sole owners of intelligence designation, but given human thought and behaviour inform most, if not all research questions within that discipline, it is a curious oversight. There was also a dearth of considerations of the philosophical questions that such explicit technological investigations introduced, aspects that are somewhat later visited by such figures as Hubert L. Dreyfus.

Dreyfus exposed the ontological underpinnings of research during much of the first phase of AI research, called ‘symbolic AI’, as rationalist. In attempting to make a machine behave as a human, symbolic AI researchers first imagined that humans’ minds operate as a formal system, where semantic meaning occurs through the assigned symbols that are related to specific objects. If a robot could be programmed with such a set of semantic systems, then they could also represent the world as they ‘saw’ it, through interpreting objects before them into recognised symbols. Dreyfus talked about this as a problem of significance and relevance, which are issues that have already been dealt with philosophically, in the existentialist tradition. He argued, from that position, that we do not experience objects in the world as symbols or models of the world, or but we experience the world itself, which robots inherently cannot do (Dreyfus 1992).

The original Symbolic Approach to AI in research became what was later called the phase of ‘good old-fashioned artificial intelligence’ (GOFAI). The so-called symbolic and connectionist AI researchers never agreed on what the more important features of intelligence are, but Haugeland (1985), who coined the term GOFAI, describes intelligent beings as demonstrating the following characteristics:

It also becomes clear that memory and ability to process thoughts and ideas and turn those ideas into analysis; the capability to make choices rather than simple decisions; and the ability for empathy and sentience are necessary for intelligence to be manifest, which is particularly important as machines are ascribed more forms of intelligence such as those argued in the second section, namely, collaborative capacities, prediction-making ability and prescriptive positioning. Marcus Hutter (2012), who designed a theory of universal AI, argued that ‘the human mind . . . is connected to consciousness and identity which define who we are . . . intelligence is the most distinct characteristics of the human mind . . . enables us to understand, explore and considerably shape our world, including ourselves’ (p. 1). AI research, Hutter indicates, reflects this sentiment, since the ‘grand goal of AI is to develop systems that exhibit general intelligence on a human-level or beyond’ (Hutter, 2012).

GOFAI reasoning sits alongside a more recent idea that machines should be fully autonomous and direct comparisons with human thought and being, in AI research, have all but disappeared. This is a problem because AI is seeing quite a dramatic return to public discussion, corporate interest and huge pots of government funding (even as austerity continues). Below, I outline the types of intelligence now being attributed to machines in the workplace context. The types of intelligence and the behaviours that are now being attributed to AI-augmented workplace tools and applications are grounded in technological autonomous decision-making, which matters because, following the pattern of judgements about ‘what is intelligent behaviour’ that informed the history of ideas around this issue, the mirror is reversing. Humans are expected to, for all intents and purposes, be controlled/managed by, but also potentially to mimic machines, as machines are portrayed to be universally reliable calculators, rather than the other way around.

These days, very little AI research is actually related to the workings of the human mind, but software engineers and designers and their users, who are in the cases referred to below, and HR professionals and managers project direct forms of intelligence onto machines themselves. But before we turn to a discussion of AI in the current context, we focus on the social theorist who contradicted other scholars who predate or inform research around how intelligence is derived and behaved: Marx.

Intelligence is dictated by material conditions

A couple of decades after Boolean published his first book The Mathematical Analysis of Logic in 1847, Marx named the ‘labour process’ as the production of use values, referring to the transformation where raw materials and labour power are used for profitability and the creation of commodities to be sold on markets. Where Marx differs from the other thinkers around him, including those listed above, was his focus on material conditions and the role of technology in the labour process. In Section I of Chapter 7 of Capital Volume I, Marx (1867/2015) wrote that ‘the elementary factors of the labour-process are 1, the personal activity of man, i.e., work itself, 2, the subject of that work, and 3, its instruments’ (p. 127). While man’s labour ‘effects an alteration’ in the material that she or he works upon, the product absorbs the appearance of the final product, where the product is a ‘use-value’ and where ‘the process disappears in the product’. The product is itself a use-value or ‘nature’s material adapted by a change of form to the wants of man’. In that sense, labour becomes materialised (but not necessarily ‘seen’).

And even before these arguments, in the Fragment on Machines, Marx (1973) had demonstrated that he was fully aware that technology is an ‘instrument of labour’, which he called a ‘thing, or a complex of things, which the labourer interposes between himself and the subject of his labour, and which serves as the conductor of his activity’ (also outlined in Engster and Moore’s article in this Special Issue). The seeming intangibility of AI clearly was beyond the scope of Marx’s imagination. Despite being the final frontier for abstracting labour in the way that Marx predicted, AI nonetheless holds implications for material conditions and workers’ livelihoods while being portrayed as an inevitable decision-maker, and so it is vital to read Marx to understand AI.

Marx observed that humans attribute our own characteristics and association, intelligence, to machines and then allow ourselves to be ruled by them in one way or another. A distinct employment relationship during early industrialisation explicitly divided people along class lines: those who were expected, on the one hand, to have mental capacities and intelligence to design machines and workplaces and to manage workers, and those who were expected to actually carry out physical labour necessary to build as well as maintain those same machines that control them, on the other.

The latter workers were, of course, not expected to demonstrate intelligence according to the expectations dominant at the time around productivity and urgency for industry to progress competitively and with colonial human resources operating in overseas enclaves. Attributing human characteristics to the machine in the Fragment text, then, reflects the ongoing Marxist view of the employment relationship as one of control, where if capitalist intelligence as held by humans is located in machines, a whole range of possibilities for exacerbated control emerge. Disturbingly, machines are also assumed to have the capacity to directly control workers and to potentially do all of our work, creating a surplus population, which introduces a question that cannot be dealt with here at length, that is, is capitalism without workers possible at all, and if not, what is next?

The next section asks, who is being asked to behave like who, or what, as we move into the era of cognitive, autonomous AI?

Who/What is the mirror? (or: the intelligence of machines, by machines?)

These days, very little AI research is actually related to the workings of the human mind, but project direct forms of (hoped) autonomous intelligence onto machines themselves. Indeed, AI research has reached a stage now where it is expected to somehow transform societies forever, with contrasting visions from the late Stephen Hawking who declared that AI was the ‘worst event in the history of our civilization’ and from Elon Musk who noted that ‘AI is a fundamental risk to the existence of human civilization’, to enthusiastic predictions about its ability to facilitate superpower status for countries.

These final sections now ask, who is mirroring who in a human/machine configuration, with all the complications that build and surround this relationship? Today, technology supplements management control over workers’ movements by the use of wearable calculation devices which capture seemingly objective data, which now can be used for algorithmically derived machine learning, used by management to make decisions not only about the ‘one best way’ to move around a factory as was sought during the period of Taylorism but also for decisions about workplace rationalisation and laying off workers who are not fit enough to keep up. Technology also helps management to reduce accountability for workers’ livelihoods and material living conditions, because the intelligent design of the workplace, whether, these days, around agile norms or within a digital Taylorist framework, furthers the core principles of productivity and efficiency through digital decision-making, with less and less human involvement and intervention. Where are the safety nets in AI-augmented workplaces?

Who controls the workplace?

Software designers and engineers are creating calculation machines for workplace decision-making and design, which are inextricably linked to the work design frameworks into which they are integrated, putting a cadre of digital experts in the driving seat for what might have once been the role of a management guru with little or no technical expertise. Chatbots are now ready to answer basic questions typed into a chat box on bank account websites. Cobots are being integrated into factories to help with picking and moving boxes across consoles. Wearable technologies with AI augmentation are facilitating on-the-spot factory training for workers. Food delivery riders are being directed to specific orders via algorithmic decision-making and judged by aggregate scores in performance metrics. HR groups are using people analytics software to select candidates for jobs on the basis of AI-driven machine learning that locates specific terms and patterns of forms of expression from CVs. Also seen in the hiring and firing phases, filmed interviews allow management to look for personality cues in speech and facial movements. This form of AI allows for ‘affect recognition’ in humans, which is also called emotion recognition and facial coding.

All of these practices are forms of automation and AI-augmented decision-making, which has implications for all stages of HR development and in industry and service work. In HR practices, it includes decisions about hiring, rationalising the workforce (firing people), appraisals, talent spotting, scheduling work programmes and selecting for promotions. These practices can and have already been shown to lead to unfair discrimination. The factory and warehouse usages of AI in robotics could lead to complete automation, where few or no training programmes are made available for workers whose jobs are replaced. The use of AI for performance analytics in call centres and in gig work such as delivery and taxi driving can lead to stress and anxiety as well as unfair discrimination.

The practices outlined here for the most recent uses of AI-augmented tools and applications in workplaces each associate specific forms of autonomous intelligence to machines, including:

Ultimately, these forms of intelligence are each oriented around capitalist expectations in the employment relationship. AI-augmented:

A conclusion: AI ‘ethics’?

In all, to gain as much as possible from work performed by humans, it is in capital’s interests to obscure labour power within the labour process, via quantification. Through construing human and machine capabilities for intelligence within work design ideal types using AI such as in human resources, gig work and robotics, experimentation may appear to reveal work’s true nature by exposing its seeming absolute value. However, the process of abstraction by numbers via specific machine/human relations attributed to machinic intelligence, at points supposedly overtaking humans, overall works to detract from the qualified experience of labour and to invisibilise suffering and the non-denumerable within material conditions.

The difference between AI and other forms of technological development and invention for workplace usage is that because of the intelligence projected onto autonomous machines, they are increasingly treated as decision makers and management tools themselves, because of their seemingly superior capacity to calculate and measure. Where many recent reports on AI try to deal with the questions of ‘what can be done’ or ‘how can AI be implemented ethically’, the issue is greater. A move to a reliance on machinic calculation for workplace intelligent decision-making actually introduces extensive problems for any discussion of ‘ethics’ in AI implementation and use, at all.

In An Essay Concerning Human Understanding, Locke (1689), an empiricist philosopher, wrote that ethics can be defined as ‘the seeking out [of] those Rules, and Measures of humane Actions, which lead to Happiness, and the Means to practice them’ (Essay, IV.xxi.3). This is of course just one quote, by one ethics philosopher, but it is worth noting that the seeking out of and setting such rules, as are the parameters for ethics depiction, have only been carried out and conducted, so far, by humans. When we introduce the machine as an agent for rule setting, as AI does, the entire concept of ethics falls under scrutiny. Rather than talking about how to implement AI without the risk of death, business collapse or legal battles, which are effectively the underlying concerns that drive ethics in AI discussions today, it would make sense to rewind the discussions and focus on the question, why implement AI at all? Will the introduction of AI into various institutions and workplaces across society really lead to prosperous, thriving societies as is being touted? Or will it deplete material conditions for workers and promote a kind of intelligence that is not oriented towards for example, a thriving welfare state, good working conditions or qualitative experiences of work and life?

This short piece has looked at how, historically, human intelligence was considered to be possible and relevant at all and then, by looking at the most recent uses of AI in workplaces, the kinds of intelligence that are now ascribed and attributed to machines. The lines of force between the subject which is humanity and the subject which is the machine are not a priori designated, so who after all is the mirror? A discussion of the role of technology and machines in the labour process as Marx outlined is, more than ever, necessary. While the human/machine mirror is subject to constant interpretation, ultimately, the social conditions of capitalism and the material conditions that are created when the use of AI is taken to an extreme in workplaces only allow for so much material interpretation, and this is what is missing in the AI debate today.