Sandy J.J. Gould, School of Computer Science and Informatics, Cardiff University, Wales, UK

This is a pre-print HTML author version of the paper. A PDF version is available. Please cite the work as:

Sandy J.J. Gould. 2024. Stochastic Machine Witnesses at Work: Today’s Critiques of Taylorism are Inadequate for Workplace Surveillance Epistemolo- gies of the Future. In Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI ’24), May 11–16, 2024, Honolulu, HI, USA. ACM, New York, NY, USA, 12 pages. https://doi.org/10.1145/3613904.3642206

Keywords: Metaverse; Workplace Surveillance; Work Measurement; Scientific Management; Taylorism; Neo-Taylorism; Ubiquitous Computing

Abstract

I argue that epistemologies of workplace surveillance are shifting in fundamental ways, and so critiques must shift accordingly. I begin the paper by relating Scientific Management to Human-Centred Computing’s ways of knowing through a study of ‘metaverse’ virtual reality workplaces. From this, I develop two observations. The first is that today’s workplace measurement science does not resemble the science that Taylor developed for Scientific Management. Contemporary workplace science is more passive, more intermediated and less controlled. The second observation is that new forms of workplace measurement challenge the norms of empirical science. Instead of having credentialed human witnesses observe phenomena and agree facts about them, we instead make outsourced, uncredentialed stochastic machine witnesses responsible for producing facts about work. With these observations in mind, I assert that critiques of workplace surveillance still framed by Taylorism will not be fit for interrogating workplace surveillance practices of the future.

Introduction

The use of digital technology for monitoring, tracking and surveilling staff is a standard feature of modern workplaces. How has digital surveillance changed, and how might it change in the future? And critically, do we have the tools as a discipline for reasoning about these changes? This paper takes the form of a metascientific study. It is not an empirical study in the typical ACM CHI style. I have not interviewed workers or managers for this study. I have not surveyed researchers. Instead, this is an investigation of scientific practice at work, drawing on past empirical work and contemporary theory to make an argument contribution¹.

I build my study in several stages. First, I will give some context on the history of measurement in workplaces and the ways that critiques of measurement practices have evolved alongside them. I focus on Scientific Management (i.e., Taylorism), its descendants, and its critiques (e.g., neo-Taylorism), but also reference the ethnomethodological tradition of workplace research. This sets up the context of contemporary workplace surveillance and the conceptual framings we use to talk about it. I then take a disciplinary turn, exploring how measurement has been constructed in Human-Centred Computing (HCC²) over time. This provides the context in which I build connections between the epistemologies of HCC research and workplace surveillance. The aim is to take a critique that is grounded in the sociology of work (neo-Taylorism) into work-based research that draws on HCC epistemological traditions.

To make the connection between emerging technologies, neo-Taylorism and HCC concrete, the next stage of the study develops a vignette of workplace surveillance in virtual reality workplaces that exist in metaverses, like the ‘Metaverse’ developed by Meta³. Do these workplaces, instrumented with the kinds of context sensors pioneered in ubiquitous computing and with interaction design informed by HCC, provide unlimited scope for workplace surveillance? Are they the kinds of workplaces that Frederick Taylor would have built 110 years ago, if only technology had permitted it? I suggest that a neo-Taylorist account of workplace surveillance would say yes; a VR workplace provides almost unlimited scope for observation and control.

I finish the study by examining whether the emergent changes to workplace surveillance technology really lend themselves to analysis by contemporary critiques of Taylorism. I argue that the epistemology of modern workplaces is changing, and that the science of work is increasingly part of a stochastic zeitgeist (after (Bender et al. 2021)). Data-driven science is often the norm, in contrast to Taylorism’s demand for planned, controlled experimentation. I claim that observation of workplaces will increasingly be undertaken by artificial witnesses, often outside the direct control of employers. With these changes, I assert that we need to be careful about returning to Taylor, recognising that by framing workplace surveillance in early 20th century management theories (augmented as they may be), means we may not be alert to changes that fall outside the scope of such theories. If we are not alert, then we may see workplaces change in unanticipated ways, compromising our capacity to defend the right of workers.

Positionality

I have come to this work having experienced a realist, often positivist training. My empirical research has largely retained a postpostivist character across a variety of research methods. Throughout this paper, I use the word ‘measure’. I view this term as being aligned with realist epistemologies, implying that phenomena exist ‘out there’, and that we just need to find a way to access (i.e., measure!) them. I have chosen to use ‘measure’ in this paper because, I believe, it is most apt to the discussions of contemporary surveillance at work, Taylorism, datafication and HCC that follow. Ways of framing what happens in work research other than ‘measurement’ would admit more diverse interpretivist or constructivist stances, and while I touch on these, they are not the focus of this piece. I acknowledge that, though I make a substantive argument for ‘measure’ being appropriate, my training and research experiences will also have significantly influenced my choice of language and constructs in this critique.

This paper is about the nature of work and its future. My orientation in discussions of work is to centre the perspectives and needs of those assuming the role of ‘managed’ over those assuming the role of manager (this is not an uncommon perspective in the CHI community (Fox et al. 2020)). This manifests in the tenor of the paper, which is critical of management and tech-firm innovation, and undertakes its conceptual development to ‘protect the interests of workers’. I accept that there are alternative framings of what follows that could, say, centre the interests of organisations, managers, shareholders, or states. I acknowledge that choosing not to take up such framings reflects my preferences and disposition, rather than any intrinsic ‘correctness’ of a position. Other papers could be written that centre the perspectives of those actors and with them in mind enumerate the consequences of the changes I articulate here. They would be useful.

Provenance

What is the motivation for this work, and what is the need for it⁴? The original motivation for this work came from observing the increasing use (and consciousness) of workplace surveillance tools (‘bossware’) stemming from the pandemic (Corbyn 2022). I also noticed metaverses entering the news media consciousness (Matthew Ball 2022). Initially, I had simply planned to use critiques from the sociology of work (neo-Talyorism, digital Taylorism) to connect metaverses and bossware in a way that would be useful for a CHI audience. In attempting this critique, though, I came to the conclusion that critiques of work measurement, as commonly formulated, are premised on ideas about measurement and workplaces that may no longer hold. At this point, metaverses went from being the primary focus of this work to a vignette that supports a broader argument about workplace research epistemologies and work surveillance critiques. I hope that that has ultimately led to a contribution that is more essential than the application of a particular critique of a particular work context would be (even if this work also doubles as that).

What about the need for this work, beyond satisfying my own curiosity? CHI has published many critical essays (or ‘arguments’) over the years (e.g., (Oulasvirta and Hornbæk 2016; Alkhatib 2021; Blevis 2018; Feinberg 2017; Muller and Strohmayer 2022; Hornbæk and Oulasvirta 2017; Gould 2022)), including ones on work (e.g., (Alkhatib, Bernstein, and Levi 2017)). I have valued these contributions because they have been able to advance arguments without being tightly bound to particular empirical results. For the parts of the CHI community that are interested in the future of work (including the computer-supported co-operative kind), virtual environments, and sensing, I felt there was a gap in the literature for a paper that related the development of new workplace tools and systems (and our understanding of them) to fundamental questions about ways of knowing (i.e., epistemologies). I have written this paper with the aim of prompting readers to consider these fundamental questions about ways of knowing as they develop productivity tools, as they capture something about avatars in virtual environments, or as they instrument working environments with new sensors. This paper should, I hope, give some ideas to readers about what they should be considering as they do so.

Human-centred computing and surveillance

The potential for connected digital technologies to surveil was recognised in the HCC literature as soon as networked digital technology achieved commodity status. Agre (Agre 1994) offered ‘capture’ over surveillance when he discussed the potential for networked machines to measure, track or surveil. The point of the distinction was to emphasise the capacity of new technologies to actively reconstruct environments based on their capacity to sense: ‘activity is reconstructed through assimilation to a transcendent (“virtual”) order of mathematical formalism’ (Agre 1994). Agre saw this happening everywhere, including at work.

Since Agre’s work, digital technology has tremendously increased both the potential scope of workplace surveillance (or capture, as Agre would have it) and the ease with which it can be conducted (Ball 2022). Human-Centred Computing researchers have explicitly investigated the surveillance of emotion (Roemmich, Schaub, and Andalibi 2023) in the workplace, as well as the power relations surveillance manifests (Anjali Anwar, Pal, and Hui 2021; Sannon, Sun, and Cosley 2022). Perspectives are as varied as methodologies, whether researchers are using justice as a frame for understanding workplace surveillance (Introna 2000), or trying to entertain the idea that workplace tracking could, in particular situations, empower workers (Holten Møller et al. 2021; Wilson and Mitev 1996). Though not described explicitly as surveillance, the HCC literature on algorithmic management and other kinds of technologies that constitute or rely on workplace measurement (e.g., (M. K. Lee et al. 2015; Zhang et al. 2022; Carlos Alvarez de la Vega, E. Cecchinato, and Rooksby 2021)) have also implicitly been studying surveillance.

Researchers have examined the surveillance of workers in roles like public transport (Pritchard et al. 2015), most published work on surveillance in human-centred computing⁵ has focused on knowledge workers (e.g., (Das Swain et al. 2023)) or platform workers (e.g., ‘ride-sharing’, online microtask platforms). Sannon et al. (Sannon, Sun, and Cosley 2022), for example, found platform workers are exposed to surveillance as part of the measurement practices of platforms. They also found that, as such workers are ‘customer-facing’, they are also subject to (often gendered (Anjali Anwar, Pal, and Hui 2021)) surveillance from their clients, too (i.e., the person commissioning a website or receiving a delivery). Altenried describes this surveillance as a way to homogenise the highly heterogenous demographic groups that these platforms require in order to maintain a sufficiently liquid labour pool (Altenried 2020). Surveillance, unsurprisingly given its negative connotations, normally appears as a point of critique in these works.

These (conscious or unconscious) investigations of surveillance at work are a subset of broader work at CHI and other HCC venues that have assessed the power of technology to surveil (Briggs et al. 2016) and the actors involved (e.g., (Sanches et al. 2020; McKay and Miller 2021; Leitão 2019; Whitney et al. 2021; Ghosh et al. 2018)). The capacity of new technologies and platforms to surveil inside and outside the workplace is clearly an area of interest (or concern) for the CHI community. This paper aims to further develop the community’s thinking about workplace surveillance.

Is there anything connecting these various accounts of workplace surveillance, something that helps us to formulate some underlying principles of surveillance? With some exceptions (e.g., (Alkhatib, Bernstein, and Levi 2017; Khovanskaya and Sengers 2019; Bannon 2011; Agre 1994)), the historical context of workplace measurement, control, surveillance does not feature strongly in HCC discourses on the instrumentation of workplaces for the measurement of staff. ‘Taylorism’ is sometimes used just as a synonym for workplace surveillance. In the sections that follow, I will go into some detail on Taylorism, and then attempt to connect it to epistemological preferences in HCC.

Work surveillance, Scientific Management, Taylorism and Neo-Taylorism

In the sociology of work literature, the increasing use of sensing technologies for quantification (Moore and Robinson 2016; Moore, Piwek, and Roper 2018) and the algorithmic control of work (Wood et al. 2018; Woodcock 2020) are, unlike a lot of HCC work, couched in the broader history of work studies. As Joyce et al. note, there can be a tendency for debates about technology in the workplace to inflate the scale of change “usually, by overstating technological novelty and understating social continuity, and ascribing decisive causality to the former” (Joyce et al. 2023, 147). A grounding in the broader historical context can help prevent this overstatement. Work in the sociological tradition often relates the datafication of workplaces (with attendant sensors and algorithms) to Scientific Management (or Taylorism), a management theory from the early 20th century.

Scientific Management

Scientific Management has a positivist approach to epistemology, and so a positivist approach to understanding work. Taylor (the originator of the theory), in defending his ideas, claimed to have implemented Scientific Management “with the object of arriving at the exact truth as to the effect of the system upon the prosperity, wages, health and contentment and satisfactory conditions of the men working under it” (Frederick W. Taylor 1914, 267). The “exact truth” demonstrates his position. The importance of being able to measure as part of the process is also impressed: “The average workman must be able to measure what he has accomplished and clearly see his reward at the end of each day if he is to do his best” (Frederick Winslow Taylor 2012, 138), as is the necessity of experimentation: “A long series of experiments, coupled with close observation, had demonstrated the fact that when workmen of this caliber are given a carefully measured task”, they perform better and, according to Taylor, become “better men in every way” (Frederick Winslow Taylor 2012, 106)⁶.

Taylorist approaches to understanding work can be seen as in contrast to more constructivist traditions to understanding workplaces. Garfinkel’s ethnomethodological approach, for example, resists the controlled, positivist aspirations of Taylorism and its descendants (see (Voswinkel 2018)). There is no sense of a dispassionate observer with truth-revealing measurements. The locus of where understanding comes from moves. As Rawls, puts it, “[t]he observer is not constructing the situation they are analysing, the participants are. Focusing on the observer at all is a problem in itself” (Rawls 2008, 724). Suchman’s work on situated action (Suchman 1987), so influential beyond work studies, takes place in this tradition.

Interpretivist approaches to understanding work have undoubtedly given us new knowledge (and new kinds of knowledge) about how work happens. The focus of this paper, though, is not on how researchers do or ought to study workplaces. The focus is on how workplaces are studied by the people that run them (i.e., managers). Whether such studies take the form of tracking, monitoring, or outright surveillance, critiques of these Taylorist-like approaches to understanding (and controlling) work are based on what is happening in workplaces. If we are talking about how work is understood within workplaces (rather than by, say, work anthropologists going into them), then, by and large, Taylorism and descendant ideas tend to dominate (Birnbaum and Somers 2022; Hill and Van 2018). This domination has, if anything, increased with the availability of digital tools that offer quantification and analysis of work activities.

Neo-Taylorism

Critiques like neo-Taylorism have been developed to analyse modern work that is organised along Taylorist lines. The idea is that the changes to work and workplace technology are “no more than a superficial change, leaving the essential aspects of the traditional Taylorian division of labour intact” (Lomba 2005, 71), while post-Taylorism suggests changes to working practices have changed too much for Taylorist ideas to retain relevance (Lomba 2005). Some neo-Taylorists argue that not only are Taylorist ideas still relevant, but that changes to work “reflect a revitalization of scientific management” (Crowley et al. 2010, 422). Gautié et al. (Gautié, Jaehrling, and Perez 2020) describe the specific role of technology in this revitalization of Taylorism, writing that it “is facilitated, but not primarily caused, by the spread of new digital technologies’’ (Gautié, Jaehrling, and Perez 2020, 777). This implies a kind of latent Taylorism, the reactivation of which is catalysed by technology. Advocates of neo-Taylorism have focused on the decomposition of tasks into atomic units as the hallmark of Taylorism, something that has become even more apparent and specialised in gig economy work (Cecchinato, Gould, and Pitts 2021; Wood et al. 2018; Alkhatib, Bernstein, and Levi 2017). In this paper, I revisit the suitability of Taylorism (and so neo-Taylorism) as a lens for understanding contemporary work measurement. My target is less the structure of work implied by Taylorism, though, and more its epistemology. I will argue that atomisation and control in Taylorism is, to a significant degree, a product of its epistemology; the modes through which it seeks to generate knowledge about what is happening at work. This has implications for the suitability and applicability of neo-Taylorist critiques of workplace technology to changing workplaces.

If we are interested in the direction of travel of workplace surveillance technologies, then it makes sense to consider developments through what is ostensibly the dominant realist-positivist lens. This is the lens that is most likely to be used within organisations as they make use of these technologies. It is not about whether work ought to be understood in this way. Given that that work is being understood in this way, what are the interactions between this form of understanding and new technologies going to be? In particular, how will these changes influence the ways that measures of work are constructed?

Digital technology is at the centre of changes to workplace surveillance. Human-centred computing provides the tools to design, explore and critique these new technologies. If we are going to use HCC research methods to understand the changing nature of measurement in workplaces (and we should), then we also need to consider the epistemological approaches of HCC research traditions. This will put us in a better position to treat the neo-Taylorist and HCC traditions simultaneously as we explore change to work measurement.

Measurement and digital sensing in HCC

CHI and adjacent HCC communities are made of up complex and varied research traditions. The publication outputs of the CHI conference suggest that we do not have a single discipline (Liu et al. 2014). Some researchers argue this is desirable (Blackwell 2015; Reeves 2015). These research traditions bring with them a variety of methods and epistemologies (Brey 2005; Harrison, Sengers, and Tatar 2011; Frauenberger 2019).

Work research in HCC is a microcosm of HCC more broadly. Work is understood through the ethnographic methods developed by Garfinkel, and transported into HCC by Suchman (e.g., (Reeves 2019)). We see controlled, interventionist, positivist studies (e.g., (Luo et al. 2018)) sitting next to those ethnographies. And we see studies in the ubiquitous computing tradition focused on instrumenting workplaces with technology, then materialising the ‘context’ as measured by those instruments (e.g., (Gallacher et al. 2015)). In this section, I draw connections between broader epistemological traditions in HCC research, and those that dominate contemporary understandings of workplaces by those responsible for managing them. I focus, therefore, on the more positivist experimental and ubicomp traditions in HCC research, because these, respectively, mirror the more formalised Taylorist traditions of workplace measurement and the less formal, going-with-the-data-at-hand, improvisational approach that manifests in situ.

The evolution of research in HCC has been described as taking place in ‘waves’ (Bødker 2015). ‘First wave’ HCC work followed in the tradition of positivist, realist science, using the epistemological machinery of psychology and engineering to understand interactions with technology (Duarte and Baranauskas 2016). Other ‘waves’ have since arrived, but this kind of first wave work is still taking place, and still making a contribution to knowledge.

First wave research has dealt with the challenges of measurement by going through a process of rarification (or, depending on your stance, reductionism). The environment is controlled. Changes to those environments are explored systematically through experimentation. Tasks might be broken down to reduce the confounding effect of one on another. If this sounds familiar, it is because workplace Taylorism is conducted on the same epistemological basis. Planning, control, experimentation, and incentive are central to generating new knowledge, whether in an HCC study of pointing performance or a Taylorist study of a production line.

The advantage of understanding workplaces through this rarifying process is that, so long as the rarification can be maintained, manipulations of the environment can have measurable effects (or non-effects) on outcomes. So long as some productivity-enhancing intervention that has been tested in a lab stays in an environment that is like the lab, then we can be confident that it will work. Of course, the disadvantage of this approach to studying interactions with technology (or within workplaces) is that it is often not possible to maintain this rarification in practice. In most workplaces, reality returns and the interventions no longer work so well (Obrenović 2014).

There has been another tradition in HCC that, rather than attempt the rarification of controlled experimental work, has instead opted for reification, to make things more concrete, and fitted to reality as it is found, rather than building rarified sandboxed realities. This ‘third wave’ has “partly moved away from a commitment to users towards a more exploratory take-it-or-leave-it approach where designers seek inspiration from use” (Bødker 2006, 2) where “[n]ew technologies servicing these developments have appeared; pervasive technologies, augmented reality, small interfaces, tangible interfaces” (Bødker 2006, 2). As part of this wave, the ubicomp tradition has focused on engineering systems that develop machines, tools and systems for building context awareness (Dey 2010; Abowd 2012).

The advantage of conducting research or understanding workplaces through this reification is that it is not necessary to try and control the environment. The goal is to sense it as it is, to be able to measure it as it exists. It’s not this straightforward, though. First wave methods are limited by the demands of their epistemologies for control, third wave ubicomp methods are limited by technical ceilings on sensing. When engineering a ubicomp system, it is necessary to consider constraints like power consumption, system availability and processing power. These constraints influence what systems are able to sense, and how they are able to sense it. For instance, for a device with little memory and a small battery, sampling from a sensor at the maximum sample rate may deplete the battery and fill the memory so quickly as to render the device useless. The challenge for those doing this kind of research is, therefore, construct validity: you know what you want to measure, but that is ultimately inaccessible, and you instead end up using proxy measures. This is what happens in the workplace; managers want to measure productivity, but in many cases that is not a construct that can be measured directly. They instead have to rely on other measures that they hope will sum to productivity, even though the individual measures are hit-and-miss (Tapasco-Alzate, Giraldo-García, and Ramírez-Ramírez 2021).

I have considered on two epistemological impulses in HCC research and related them to our understanding of workplaces. One is the rarifying tendency. In this tendency, control is exerted over the environment to make the environment easier to measure. This comes at the cost of a poor understanding of reality where control is disapplied (e.g., when an experimenter or manager stop looking). The other tendency is the reifying tendency, in which researchers have favoured trying to measure reality (i.e., context) as accurately as possible with increasingly sophisticated sensors. This comes at the cost of construct validity, with the gap between an uncontrolled reality and what it is possible to measure making it difficult to be sure that what has been measured is sufficient to characterise the context. As a result, you end up not measuring the things you need to, or, worse, having what you can measure become an accidental target (Treem et al. 2023).

If you want to understand a workplace, then these rarifying and reifying impulses seem to be in a kind of contradiction. You can attempt to exert control to construct an abstract reality that you can reliably measure, but accept that if your control fails, the reality falls apart. Or you try to measure reality ‘as it is’, but accept that your instruments are inadequate to some degree and that you can’t really measure the thing you want to⁷. Could the solution to these tensions be to create an environment that yields more control (no wondering what staff are ingesting, imbibing or inhaling on their breaks) that also offers greater capacity, ease and fidelity of instrumentation (no annoying sample rates or batteries to worry about)? If such an environment exists, would it offer the aspiring ‘Scientific Manager’ a way to ‘do’ Taylorism, but without having to set up an expensive paternalistic model village next to a factory to try and control the environment as much as possible (Jeremy 1991)? I will consider whether virtual reality workplaces, something pioneered by HCC researchers, fit this bill.

Working in virtual reality workplaces

The ‘Metaverse’ is the way that Meta, the company that owns Facebook and other social media platforms, has been thinking about human interaction of the future. The idea is that people will enter digital worlds through virtual reality devices, which will mediate interactions with other people. This idea is not new. Platforms like Second Life have in the past offered similar digital worlds (Bessière, Ellis, and Kellogg 2009).

Metaverses are useful here because it has been proposed as the venue for virtual working environments (Purdy 2022). The idea is that rather than working from home or in the office and then making use of tools like Microsoft Teams or Zoom for video calls with your online colleagues, or having in-person meeting with others in your office, your workplace is in a metaverse. Regardless of where you are, you don VR equipment, and your meeting spaces are in the virtual world. Your software tools are in the virtual world, and you interact with them through your avatar. You no longer have a real workplace, you just have locations where you can join your (virtual) workplace. (Though see Richardson (Richardson 2023) on how infrastructures of work, rather than sites of work, are responsible work’s construction.)

Let’s set aside whether your workplace existing only in virtual reality will actually ‘work’. Most likely, it will kinda-sorta-maybe-but-not-really work. There will be some use cases where it seems to do the job, and others where its use will be catastrophic in some way. As it has done for the last thirty years, research in the Computer-Supported Co-operative Work (CSCW) literature will continue to explore which contexts are best for VR work, why the things that work, and how we can translate the things that work to other domains (Wexelblat 1993; Freeman and Maloney 2021; Fraser et al. 2000). I am not trying to make any determination about the efficacy or otherwise of these technologies in this work. Metaverses just provide an excellent lens through which we can simultaneously view workplace surveillance, Taylorism and its critiques, and epistemologies of HCC.

Surveillance in metaverses

An employer decides they have a problem with a business process. They decide they need to conduct a study in order to understand what is going wrong. Another employer is anxious about whether staff are productive while they are working at home. They decide they need to keep an eye on staff in order to make sure that they are working⁸. In both cases, employers will want to identify things to measure, because measurement, in the tradition of Scientific Management, is typically how evidence is created in workplaces. Measuring things about work is difficult, though (Gould et al. 2023), as we have already considered.

Metaverse technologies are often promoted by their creators as being “primarily as an extension of workers’ physical and cognitive agency over a variety of workplace materials and activities […] rather than a more radical transformation of workplaces’ ability to monitor, track, and evaluate worker efficiency” (Greene 2023, 19). Park et al.’s work (Park, Ahn, and Lee 2023, 3), published at CHI 2023, points to a similar orientation in pre-pandemic academic literature on virtual working environments: that metaverses exist to free workers from the spatial, temporal and social constraints of the physical workplace.

Some recent work has continued to present metaverses as locations for more productive, more flexible, ‘better’ work. Adhiatma et al. (Adhiatma, Nurhidayati, and Fachrunnisa 2023) describe the ways in which metaverse workplaces can be ‘maximised’ to encourage productivity, flexibility and connectivity. The need to train employees so that they have the requisite skills to be productive has been identified (Hawkins 2022). The potential (need!) to develop new key performance indicators that are suitable for (and that leverage) metaverses are described from the perspective of productivity (Tsappi and Papageorgiou 2023), without considering their potential for surveillance: “Immersive engaging interactive experiences can be determined in virtual recruitment by use of behavioral analytics, haptic technologies, deep and machine learning algorithms, and emotional state prediction tools” (Gheorghe H. Popescu et al. 2022, 22). Those engaging experiences are, presumably, to the benefit of workers, given that the problems such an environment might produce for workers are not enumerated.

At the same time, researchers are also developing critiques of metaverse workplaces. Szakolczai, after Agre, describes these technologies as being part of a ‘captaverse’ (Szakolczai 2023) which normalises abuses of data. Egliston and Carter (Egliston and Carter 2021), coming from a Critical Data Studies perspective, focused specifically on the capacity of metaverses for mass data collection, enumerating the ways in which the capability to sense gives platforms power over those subject to them. Park et al.’s empirical work on perceptions of metaverse workspaces yields a less consistently sunny picture, too: workers are concerned about the potential for surveillance and tracking, and have resisted accordingly (Park, Ahn, and Lee 2023).

Metaverses and HCC epistemologies

Metaverses bridge the rarifying and reifying impulses in HCC research. In this environment, there is no need to create constrained worlds in which constructs can be more reliably and robustly developed. There is no need to be constrained by the limits of what physical sensors are able to sense. Everything that exists in the virtual world can be instrumented. Things that we might normally need an experiment to be able to sense, like reaction times, become trivially accessible in an authentic working context. We no longer need to be constrained by the physical limits of sensors and batteries. We can develop complex sensors in the ubicomp tradition that allow us to sense context, but we no longer need complex, expensive and error-prone machine vision systems to keep track of where people are. We don’t need to rely on proxy measures (like, say, pressure changes in a ventilation system (Caceres and Friday 2012)) in order to track people’s journeys through environments. Arditi has described these environments as a new form of enclosure: taking the spaces through which people live their lives and subjecting them to new schemes of control and ownership (Arditi and Arditi 2023).

Lee et al. (L.-H. Lee et al. 2021) argue that while the term ‘metaverse’ was originally coined to refer to “a massive virtual environment parallel to the physical world, in which users interact through digital avatars” (L.-H. Lee et al. 2021, 1), the metaverse of the future will blend physical and virtual worlds seamlessly. As Wang et al. (Wang et al. 2023) put it, this kind of metaverse is “a fully immersive, hyper spatiotemporal, and self-sustaining virtual shared space blending the ternary physical, human, and digital worlds” (Wang et al. 2023, 317). For such shared space to exist, we’d have to conceive a way to instrument the physical and human worlds such that they were entirely intelligible to the digital world. This would mean solving the problems of ubicomp, and implies, once again, an infinitely instrumentable world that where everything that one needs to measure can be measured. (How else could the connection between those three spaces be seamless?)

The challenges of construct validity remain, of course. If you take it as read that phenomenological states are largely inaccessible by quantifying behaviours or physical attributes (e.g., using facial recognition to ‘detect’ emotion), then there remains plenty inaccessible to those running a metaverse. Taylorism and its descendants have generally shown little interest in being able to measure these kinds of things, though. Where they have, they have generally taken a realist position on them – that all of the world is accessible and that you can measure emotion from people’s faces.

Given my analysis, metaverses seem the ideal environment for a Taylorist approach to measuring work, and consequently for surveilling staff. There are no unobserved corners for workers to take themselves to. Brain sensing isn’t quite there yet (Afergan et al. 2014), but it might be eventually. There are no parts of production that are too challenging to observe. Interactions between workers that might otherwise be challenging to monitor are now mediated only through a metaverse. If those interactions are hard to measure, then you can adjust how interactions are mediated to make it easier for you to measure (e.g., through task design). Everything can be done with code. A Disneyland for an aspiring Scientific Manager.

Retooling neo-Taylorism for a stochastic zeitgeist

On the surface, then, a VR workplace seems like it would be the ideal territory for Scientific Management of one kind or another. Almost unlimited potential to instrument every tool and every task means a commensurate potential for work measurement, and hypothesis testing.

There is a sense in which Taylorism has become a byword for workplace surveillance. More sophisticated technologies that permit an expansion of surveillance, means, ultimately, a reinscribing of Taylorist principles into new working domains (Kittur et al. 2013; Cherry 2015; Altenried 2020). I am not sure that what is happening is as straightforward as ‘new Taylorism, same as the old Taylorism’, though. And I am concerned that channelling critiques of workplace measurement through neo-Taylorism means that we risk applying arguments couched in older positions on the structure of work, measurement and science to contemporary contexts where there are changes in epistemology. The risk is that we miss out on important aspects of what is changing and so are in a worse position to design for (or against) it. My focus for the rest of this paper is not on whether employers seek to discipline through new digital surveillance, but is instead on what a Taylorist framing of this relation does for our ability to understand the ways in which it is changing.

The epistemology of ‘true’ Taylorism

As discussed earlier, Taylorism is typified by the control, planning and atomisation of work that allow it to be pushed through experiments that provide measurements for management. Following Braverman (Jonna and Foster 2014), it has been described as the “appropriation of workers’ autonomy and control over their work, the construction of a politics and a technology of the disciplined body at work, constituted [one of the] principle[s] of Taylor’s system” (Bahnisch 2000, 55). This control is then viewed as proceeding “by acting on class and sexed subjectivity” (Bahnisch 2000, 63). Without seeking to minimise these aspects of Taylorism, control in Scientific Management and its descendants is also being exercised in the most positivist sense of experimental control, that is to say, exerting control over people and their environments to make the workplace more laboratory-like and allowing for experiments that test hypotheses and allow causal relationships to be established.

Employers designing rigorous experiments, with clearly operationalised measures and testable hypotheses: if that was ever happening at large, is that what is happening today, and does it look like what is going to happen in the future? It is not clear that it is what is happening, or that it will be what happens in the future (Khovanskaya and Sengers 2019). Instead, we are at a point where a common strategy is to measure whatever can be measured, and try and rake through what has been collected using machine learning and other statistical tools to try and work out what might be happening (Newlands 2021). This isn’t about the use of those statistical techniques in science: as Gigerenzer et al. (Gigerenzer et al. 1989) discuss in their history of probabilistic techniques in science, relying on the fuzziness of probability and statistical techniques has been essential to a variety of scientific endeavours for hundreds of years. It is rather about the larger process of scientific practice, and what research communities consider acceptable epistemologies. Moves in how science is undertaken will have significant implications for ‘Scientific Management’ and critiques of it.

Data first, ask questions later.

Kitchin’s (Kitchin 2014) influential work, building on ideas by Anderson and others (Anderson Jun 23, 2008 12:00 PM), elucidates this point clearly. It makes the point that the capacity to store and process massive amounts of data has changed the epistemology of (some) scientific practice. Kitchin notes the position of some researchers “suggest[s] that a new mode of science is being created, one in which the modus operandi is purely inductive in nature” (Kitchin 2014, 4). The paper argues that this is not what is happening – there is no unplanned emergence of theory from a bag of data – but instead this new kind of epistemology “is situated and contextualized within a highly evolved theoretical domain. As such, the epistemological strategy adopted within data-driven science is to use guided knowledge discovery techniques to identify potential questions” (Kitchin 2014, 6). This is more nuanced than ‘data first, ask questions later’, but it is a far cry from the heavily controlled positivism that characterised Taylorism and that provides the fodder for neo-Taylorist critiques of workplace surveillance. Can we even be said to be engaging in a neo-Taylorist critique if contemporary workplaces do not require control as an epistemological prerequisite? Employers may still crave control, absolutely, but not necessarily because control is required before scientific investigations of a workplace can take place.

Kitchin’s paper was published a decade ago, at a time when Big Data was the buzzword. “Artificial intelligence” appears in the paper once, and “machine learning” twice. This doesn’t dilute the significance of the contribution: the kind of data-driven science that the paper describes is increasingly influential, and, with datafication an activity seemingly promoted as something for every organisation to engage in(Chamorro-Premuzic et al. 2017), increasingly the starting point of investigations. However, the rapid advances of AI and ML technologies over the last few years is changing not just how science is being practised, but who or what is practising it.

Stochastic machine witnesses

Latour relates the development of empirical science to Boyle: “Instead of seeking to ground his work in logic, mathematics or rhetoric, Boyle relied on a parajudicial metaphor: credible, trustworthy, well-to-do witnesses gathered at the scene of the action can attest to the existence of a fact, the matter of a fact, even if they do not know its true nature. So he invented the empirical style that we still used today” (Latour 1993, 18). The key to this invention was the acceptability of reliable witnesses as a means of producing new knowledge. Empirical science still works in this way; we have mechanisms for assessing the standing of witnesses, and we use these to decide whether we trust the way that they have witnessed phenomena⁹.

Machine learning can aid the analysis of data in ways that are not all that far from traditional statistical techniques. These kinds of statistics are an accepted part of conducting realist, positivist science. The current direction of AI technologies suggests a departure from this, though. If we are, say, to use large language models (LLMs) to help make sense of large datasets¹⁰, then we get to the point where we are introducing new actors in the process of doing science who are now taking on the role of witnesses in the scientific process. This fundamentally changes the relationship between phenomena and their witnesses. We have established ways of establishing the credibility of witnesses to phenomena¹¹. One way is to require witnesses to complete research degrees before we assess credibility to them. Will we permit artificial witnesses, and, if so, how will we assess credibility to them? This is not just about the capacity of things like LLMs to ‘hallucinate’. Human witnesses to phenomena do the same thing. It is a more basic than that, it is about who is permitted to observe, who is permitted to ‘do’ science (F. Lee 2023).

What this means for critiques of workplace surveillance

What does this mean for Scientific Management, or neo-Taylorism? It means that the way we are doing science has changed, and the way we might be doing science in the future might change even further. The ability to instrument everything, collect all possible measurements and work out what it means is not compatible with the Taylorist, positivist, realist approach of Scientific Management. The means to create witnesses to phenomena that are not human could fundamentally change all scientific enterprise. So what does the ‘Scientific’ in ‘Scientific Management’ actually mean? It points to a conceptualisation of science that has dominated the last hundred years. Now we’re moving into a more stochastic zeitgeist, where we rely on non-human witnesses who, just like human witnesses, can return different responses to the same stimulus, it seems that we shouldn’t take it for granted that the possibilities of a metaverse, as interpreted in our new zeitgeist, can be properly explored with conceptual tools like neo-Taylorism that have been developed under certain assumptions about measurement and workplace science.

Manokha (Manokha 2020) has recently argued (after Foucault) that digital technology has fundamentally altered power relations in the workplace, and that technology affords employers a fully-featured panopticon in which their disciplining gaze is omni-present. Similar arguments have been made about the power of algorithmic management, and its capacity to disenfranchise workers (Franke and Pulignano 2023). I do not disagree that the trajectory of these technologies has been to change power relations in workplaces. This fits with a neo-Taylorist account of what has been happening in workplaces over the last couple of decades; more measurement, more employer control, more employer power. Given the account I have given of the changing epistemology of the workplace, though, that neo-Taylorist account feels disintermediated. The implication is that the disempowerment of workers and the empowerment of employers is dyadic. One’s loss is another’s gain. Yes, workers have lost power. And so far it looks like that power has transferred to employers. At this stage in the development of these technologies, it looks like this because the employers who have had the capacity to implement the most powerful AI technologies have done so in a vertically integrated way. When Uber develops new methods of algorithmic management, or Amazon builds new sensors for monitoring staff, they do so with the capacity to make sense of the data collected. They do so with more organisational understanding of the new (and still limited) artificial witnesses to phenomena it is deploying to measure the world. The power of these companies to use vertical integration is not limited to controlling consumer markets (Zygmuntowski 2022), it can be used internally to control labour, too.

How do the power relations look outside organisations without the capacity to vertically integrate sensing, measurement, analysis and action? Some –most, the vast majority, even– of organisations will not have this kind of capacity. Instead, they will be consumers of these workplace technologies (Gould et al. 2023). What will it mean when organisations, convinced of the necessity to instrument their workplaces and surveil staff to stay competitive, contract-out the witnessing of phenomena in their workplace to artificial agents (leaving aside the question of whether more data can even solve their problems (Dijck 2014))? How will they assess the credibility of those witnesses? What would an LLM, or whatever, for surveilling staff need to do to be awarded “chartered status” like HR professionals¹²? Where does the power lie when these agents come with a monthly subscription and their proprietary nature makes it impossible to interrogate them? Yes, of course, still to the employer, but also to another set of entities not under the control of employers, the organisations selling access to the artificial actors whose behaviour is not deterministic.

For a neo-Taylorist account of work surveillance to be complete, then, it needs to be able to expand to fit these new actors. An account that considers a simple tug-of-war between employers and workers is going to end up missing critical activity that is not taking place within that relation. We will perhaps need to augment these accounts of workplace surveillance with our understanding of trust in human-in-the-loop systems (Middleton et al. 2022). We may need to look to the Business Management literature on outsourcing (Langfield-Smith and Smith 2003) to try and make sense, operationally, of what kinds of control systems might be established. And all of this has to be connected to the essential epistemological question of what can be measured, and how it can be measured. It’s only by doing this that we will be in a position to really understand what we are talking about when we’re talking about workplace surveillance. Until we have that nailed down, neo-Taylorist accounts of the erosion of good work by surveillance are not going to be adequate to critically evaluate the changes that are coming with AI in the workplace. Ironically, our capacity to defend the rights of workers at work might end up being contingent on resisting data-driven science, where employers get to decide what to investigate post-hoc, and re-embrace the upfront, controlled, planned –and potentially negotiable!– scientific practices of Taylorism.

Discussion

Having made my argument about workplace surveillance and the ways in which we think about it, there are two things that it would be valuable to consider. The first is what people designing and building the future of work should take away from this argument. The second is to reflect on how my argument relates to the human-centred computing community beyond work and workplace studies.

The design of the future of work

Suppose that you are involved in designing, understanding and building workplace technologies. Lots of people at CHI are! And then suppose that you accept the argument that I have made here. So what? What now? I do not think there are ‘implications for design’ from this work; the argument that I am making is not specific enough to a particular context. However, I do think there are ‘implications for thinking about design’ that are relevant to those creating workplace technologies. To keep these implications somewhat digestible, I offer an enumeration:

1. Epistemologies of work apply. Yes, your work too! These are not remote ideas for academic-types to mull over. If you have a new workplace productivity tool that, say, monitors activity to make recommendations about when to take breaks, then the things that you are able to measure and decide to measure during development could end up setting normative expectations for behaviour in workplaces. And vice versa: what you measure is influenced by existing normative practices of measurement in workplaces. Be reflexive about the potential for these scenarios. Think about what your tools and systems will be able to ‘know’ and how that knowledge is obtained. If constraints mean that your construct validity is weaker than you’d like, perhaps think about how you can design with seams, helping prospective users appreciate these limitations (see (Chalmers and MacColl 2003)).

2. Think about data, even if you’re responsible for interaction design. Is anyone designing commercial interactive systems these days without a significant data layer? Even take out pizza companies are instrumenting their websites to track your mouse pointer¹³. If you are not responsible for that data layer, what will the affordances of your interaction design be for that layer? The Human-Data Interaction framework proposed by Mortier et al. (Mortier et al. 2015) is a good tool for thinking about this. What agency will users have over what is ingested by your system? To have agency, what is being collected and how it will be used will need to be legible to users. Finally, are there ways that you can build negotiability into tools, systems, interactions? Can people opt to share more or less, and still enjoy a good experience commensurate with their preferences?

3. Critiques scaffold thinking. We need to form habits of critique. Critiques of workplace surveillance like neo-Taylorism or digital Taylorism provide ways of thinking about work, power and measurement in ways that are consistent over time and contexts. This is useful, given the breadth of workplaces that the CHI community is interested in; software engineering, healthcare, crowdwork, education, research. The full gamut. Applying well-developed critical perspectives provides a starting point for comparison and shared practice. Members of the CHI community have been pushing critical practice for twenty years (e.g., (Dourish et al. 2004; Sengers, McCarthy, and Dourish 2006)). The conference has a submission track specifically for critical research. In the same way that working with people implies a set of ethical practices and norms, I would like us to get to the same place with work research. If you design or build workplace technologies, you would get into the habit of applying critiques. You don’t have to agree with what those critiques say about what you’ve created, but if we can make reflexivity a habit, we may be able to better anticipate harm (Soden et al. 2019). The regular application of critique keeps those critiques in good fettle, too; the more of us that apply them, the more we can hear them groan and strain as the world changes.

4. Machine intelligence will mean revisiting the previous three! If you’ve set up a workflow that lets you think about epistemology, critique and data when you’re specifying, designing, building workplace technologies, then job done? Unfortunately, my thesis here is that machine intelligence has the potential to remake these things. New epistemologies. Critiques unmoored from the state of the art. Data produced by artificial witnesses…and we’re not sure how they are witnessing. We will need to iterate as the capabilities of these machines change over time. What “collect”, “process”, “store”, “analyse” mean for workplace data will not be static. When they change, how will the tools and systems you’ve built look? What assumptions are you relying on? As a discipline, we have developed ways of using speculation that might be useful for exploring these kinds of challenges (Forlano and Halpern 2023).

Connections beyond the workplace?

This paper is about how we measure work. I have used virtual work environments as a lens with which to examine critiques of work measurement, assessing these critiques against the state of the art in workplace surveillance. How do these ideas about work connect to other kinds of HCC and HCC-adjacent research? The purpose of this paper is not to provide a generalisable account of surveillance or attendant epistemologies or critiques. The focus is specifically on workplaces. However, there are related ideas that members of the CHI and broader HCC communities have been working on, and it makes sense to consider how the argument I have made here relates to this work.

This paper is closest to publications at CHI on datafication and algorithmic decision-making. This work is often about how models of the world are created through different ways of knowing. Muller and Strohmayer (Muller and Strohmayer 2022), for instance, write about ‘forgetting practices’ in data science. Which data in a given context is kept. Which is thrown away. This is a critical issue in the workplace, where workers may have no control over how their organisation chooses what to ‘remember’ or ‘forget’. How will artificial witnesses modulate the ‘forgettance stack’ that Muller and Strohmayer describe? Given the complexity of this stack, are our current epistemologies sufficient to even know?

There are connections¹⁴ between what I have presented here and Alkhatib and Bernstein’s work on algorithmic decision-making (Alkhatib and Bernstein 2019). They note that it is often the case that individuals close to decisions make ‘street-level’ judgements to bridge policies, laws and reality: “A police officer chooses whether to issue a warning or a traffic citation; a judge decides whether to allow a defendant to pay bail or to have them remanded to jail; a teacher determines whether to waive a course’s prerequisites for a student. These decisions often involve nuance or extenuating circumstances, making it all but impossible to prescribe the right response for all situations” (Alkhatib and Bernstein 2019, 2). The point is that algorithms are not capable of making such adjustments. This is relevant to the crowdworking context that Alkhatib and Bernstein explore, but also to workplaces more generally. Evaluating performance at work often involves street-level decisions by managers – accounting for the broader and individual context of a particular marginal case, where protocol and reality sit uneasily together¹⁵. Street-level arbitrations are not always fair. Employers often selectively enforce policies to target specific individuals capriciously, but what about where you have artificial witnesses reporting to street-level algorithms, and this is siloed in a proprietary decision-making stack? We might, as with forgettance, not even be in a position to inspect the underpinnings of workplace decisions.

This work also connects with the Science and Technology Studies work Pine and Liboiron have published at CHI (Pine and Liboiron 2015). Their work deals with fundamental questions about measurement and the construction of new things through measurement. The contention is that decisions about what to measure are intrinsically political, and that the ability of measurement to construct new things can be used to advance political goals. In the context of workplace measurement, this implies ontological questions alongside epistemological ones: the decisions that are made about measurement are not just about ways of knowing what is happening in a workplace. The measures themselves –of course they do– produce new things in the workplace. The corollary is clear: if you outsource work measurement in your organisation, you are outsourcing the creation of new things (actors? beings?) in your organisation, too. The essentially political nature of measurement (including that by artificial witnesses: their make-up is political, too (Mehrabi et al. 2021)) means that the creation of new things will be political, too. In future work, it would be useful to consider the connection between ontology and epistemology in the workplace in more depth.

Given the interest of the CHI community in datafication and epistemologies more generally, there are almost certainly other connections to make with work already published. It may be that I have yet to encounter this work, it may be that I have but have yet to appreciate its relevance. My hope is that this paper will be of interest to a broad CHI audience and that perhaps, down the line, other authors will read and cite this work and help me make some of those missing connections.

Conclusion

The future of work is an important topic of research for CHI and CHI-related communities. A topic of such importance, especially one in which researchers are enacting change, requires a well develop metascientific underpinning. We need to understand what it is that we as a community are doing and why we are doing it in the way that we are doing it. The purpose of this paper was to explore a particular aspect of future of work research that has growing salience: digital surveillance.

Via a vignette of infinitely instrumentable workplaces in metaverses, I have made the argument that neo-Taylorist critiques of workplace monitoring, and their focus on control, are not, at the moment, equipped to defend the interests of workers in changing workplaces. Epistemologies are changing. The controlled, positivist science of Taylorism is getting harder to make out in the contemporary practice of workplace science. Artificial witnesses to phenomena will change the way that we credential the actors that are trusted to observe workplace phenomena. I have sketched the limits of our current tools for critique. I hope this is another potential starting point for the collective development of our critique of work technology, a development that will help to protect the interests of workers as workplaces change.

I am indebted to six anonymous ACM CHI 2024 reviewers for the attention they gave to this manuscript.

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