Geographies of artificial intelligence: Labor,surveillance,and activism

Abstract

French

This article reviews geographic work on artificial intelligence in the context of labor, surveillance, and activism, paying particular attention to developing strengths, as well as current gaps, in the discipline's critical engagement with this emerging topic. Across its sections, we frame artificial intelligence as a societal transformation that cannot and should not be contained to one field or subdiscipline within geography, arguing, instead, that this emerging technology must be drawn into conceptual and empirical debates within all parts of our scholarly community. To conclude, the article identifies ways that geography, especially critical human geography, can contribute to better understanding the complicated and proliferating geographies of artificial intelligence in the world around us and bring a multi-faceted framework to discussions of this disruptive technology.

Keywords

Artificial intelligence emerging technologies labor future of work surveillance activism resistance

Introduction

As nearly every publication—academic or otherwise—points out, artificial intelligence (AI) is permeating more and more aspects of our daily lives. A disruptive technology in every sense of the phrase, AI is reconfiguring how economies work, how borders are monitored, how healthcare is priced and performed, and how many of us find music, partners, restaurants, and jobs. Artificial intelligence is the subject of government regulation in some places, investment speculating in others, and military innovation around the world. Tools like ChatGPT raise questions for educators about how to detect its use in student work, while wider developments in generative AI raise questions about its impacts for knowledge and creative workers more broadly. From the world of art to the art of war, AI demands critical reflection on how work is performed, how decisions are made, and what it means to be human.

These and other questions drive an expanding literature on AI as an emerging technology. A review of this full literature is beyond the limits of what one article can address, so here, we undertake a more modest task of reflecting on geographic scholarship on AI in relation to themes of interest to this journal. Human Geography publishes research at the forefront of radical geography that advances critical, liberatory, leftist, social, and/or environmental justice scholarship. Thus, we focus our review on the entanglements of AI and labor, particularly the future of work, surveillance, particularly state and environmental applications, and activism, particularly AI as an impetus for political action. The list of themes that could be covered in an article like this one is long and growing: AI and racialized bias (Birhane et al., 2022; Cave and Dihal, 2020; Mirzoeff, 2020), AI and data colonialism (Hao, 2022), algorithmic governance (Zook and Blankenship, 2018; Gritsenko and Wood, 2020; Kotliar, 2020; Ettlinger, 2023), the uneven applications of AI (McDuie-Ra and Gulson, 2020), and AI's impacts on geopolitics and weaponization (Goode, 2021; Shaw, 2017), just to name a few. We selected our three themes (labor, surveillance, and activism) in an effort to make the case for critical geographic engagement with AI to the readers of this journal and see this article as part of wider conversation undertaken by many of the scholars cited above.

The sections that follow begin with a brief discussion of AI as a concept, set of technologies, and constellation of practices, paying particular attention to critical geographic work on the topic. From there, we move through the three themes listed above to map out both how they are increasingly shaped by AI and how critical geographers have reflected on this shaping.¹ Across its sections, this article develops what might be a simple argument, yet one that bears stressing at this early stage of geography's engagement with AI: AI is a societal transformation that cannot and should not be contained to one subdiscipline or field but, instead, must be drawn into conceptual and empirical debates and pedagogy within all parts of our scholarly community.

What is artificial intelligence?

Sometimes linked to computer science and engineering, increasingly connected to law, philosophy, and public policy, and infusing fields from public health and medicine to social work and demography, AI, as both an object of analysis and a research tool (e.g. Kwan, 2016), is transforming the landscape of academic scholarship and raising questions for all disciplines, including geography. Artificial intelligence is difficult to define in precise terms. As Goode notes, AI “is perhaps best understood as a combination of existing technologies and practices related to automation, cognition and algorithmic interaction” (2021: 2) or, as cited in Clifton et al., 2020 (p. 7), “a set of technologies that can imitate intelligent human behavior (KPMG, 2019, 3).” Building on this last point, a common reference point in discussions of AI, including in geography (e.g. Lavallin and Downs, 2021), is the Turing Test, designed by Alan Turing to check if a machine is artificially intelligent. To pass the test, a computer requires natural language processing, knowledge representation, automated reasoning, and the ability to learn from the environment (Muggleton, 2014).

Artificial intelligence is not new, and some scholars point to waves, or even seasons, in its popularity, advancements, and applications.² What is more recent, however, is the incorporation of AI into so many facets of social, economic, and political life (see Goode, 2021; Bryson, 2019). Because of AI's power to shape human experiences, it is frequently framed as a paradigmatic shift and a revolutionary break from the past (e.g. Rashid, 2021). Rob Kitchin, for example, an early leader in both digital geographies and geography's engagement with AI, remarked that approaches rooted in AI might enable “an entirely new epistemological approach for making sense of the world; rather than testing a theory by analyzing relevant data, new data analytics seek to gain insights ‘born from the data’” (2014, 2). As Ifeoma Ajunwa (2020) quipped, with the arrival of AI, “The scientific method is dead” (1), and models based on sampling now dwell alongside models based on big data and algorithms that learn and change over time.³

While Ajunwa's claim about the death of the scientific method might be debated, within much academic (and gray and popular) literature on AI, there is a strand of work suggesting a fundamental difference about the age of AI (e.g. Hey et al., 2009). With that context in mind, how have geographers studied a set of technological advances that are often best described in science fiction and poorly understood by those tasked with regulating them? Like other disciplines, geography has focused on AI as an object of analysis, raising key questions about how AI systems see and create the world (e.g. Amoore, 2019). More than 30 years ago, Helen Couclelis (1986) attempted to delineate the “human” or “social” in relation to emerging technologies by asking what AI approximated vis-à-vis human learning and thought processes. More recently, Del Casino et al. (2020, 2) prompted us to consider “what sorts of human and non-human subjectivities are made possible and/or closed off by the rise of new robots and robotic knowledges.”⁴

Beyond these questions about how AI systems produce knowledge, spatial questions have featured prominently in geography's engagement with AI (e.g. Alvarez León, 2021; Attoh et al., 2021). How AI travels across space and societies, as well as the geographies produced in and through AI, has been a dominant concern (e.g. McDuie-Ra and Gulson, 2020). Ho et al. (2020), for example, write about the challenges of algorithms trained on one demographic group being used on another, and later in this article, we lay out how geographers have helped articulate the uneven geographies of production, application, and responses to AI. What much of this growing literature shares is an effort to trouble AI's objectivity by illustrating how AI systems embed societal and spatial structures (e.g. Alvarez León and Gleason, 2017; Amoore, 2020; Birhane et al., 2022). The worlds that AI constructs reveal the deeply transformative potential of these technologies beyond an extension of digitality (Walker et al., 2021).

It is worth noting a growing set of arguments about who, in human geography, can or should engage AI as an object of analysis and how. Renee Sieber, in a co-authored interview (Janowicz et al., 2022), recently asked, “Are human geographers willing to hear that they need to know more about AI than a superficial understanding of what they read in someone else's critique?” (449), raising a question that also shaped debates over GIS in the 1990s. In the same interview in which Sieber asked this question, she and her co-authors debated how much agency both humans and algorithms possess and what a field like counter-GIS might offer for thinking about a counter-AI.

There are also conflicting views in geography about what AI is most like and where it fits vis-à-vis geography's subfields. Kwan (2016), for example, has written about “algorithmic geographies” as part of geography's wider engagement with big data, developing arguments about “algorithmic uncertainty” which link to, but are also different from, theoretical work on the embeddedness of doubt in algorithms (Amoore, 2019). Lavallin and Downs (2021) place AI in relation to GIS, remote sensing, and GIScience in what some are now calling GeoAI, tracing the history of geography's engagement with AI at different points in time. Maalsen (2023) positions geographic attention to algorithms as of a piece with the discipline's “digital turn,” while others place this focus within the context of digital geographies (e.g. Ash et al., 2018). However and wherever AI is placed in geography's scholarly structure, the thread connecting these efforts is the argument that geographers must “remain attentive to the omissions, exclusions, and marginalizing power of big data,” algorithms (Kwan, 2016, 275), and, by extension, AI.

Labor

Artificial intelligence's implications for labor are significant (Clifton et al., 2020), not only because of its potential to shape labor markets via hiring practices but also because of its potential to shape what jobs are even available for human workers. Although many early conversations around AI's consequences for work focused on jobs described as the 3Ds (dirty, dangerous, and dull), there is growing evidence that AI will disrupt many other forms of labor, including creative work associated with everything from journalism to film to music. Artificial intelligence is bound up with platformization and the global proliferation of digital labor platforms, which, as Matthew Cole (2023) recently noted, increasingly function as a form of infrastructure without traditional accountability mechanisms or links to notions of the public good (see also Murakami Wood and Monahan, 2019). It is also bound up with logistics, and, as the COVID-19 pandemic made clear, much of the work that keeps goods, capital, and people flowing (and healthy) can be accomplished by “advanced automation” (robotics, AI, and software) (Lin, 2022, 464).

This trend toward automation and autonomy reaches its apex in what Weiqiang Lin (2022) calls “automated infrastructure”—“an all encompassing infrastructural framework marked by the primacy of virtualized transactions” (p. 464). In this automated infrastructure, humans are not only paired with and interdependent on machines but also potentially expelled from the labor process altogether, with “profound uncertainties” for the future of work and workers (ibid.). This potential “replacement” stretches beyond supply chains, infrastructure, and logistics, as AI and advanced automation are brought into decision-making processes in government, business, healthcare, law, and other domains of daily life and work, if not without pushback and concerns (Lin et al., 2022). At the same time that AI has the potential to reconfigure labor forces and labor processes across sectors, it is, paradoxically, also deeply dependent on human labor. As Renee Sieber (Janowicz et al., 2022) recently put it, the “human-in-the-loop really is humans as workers for most applications” (448).⁵

What is less clear at this early stage of AI's transformation of labor is how the specificities of place—infrastructures, work cultures, labor histories, global connections, and so on—will shape the ways that AI affects labor and that workers respond to, appropriate, and resist those impacts (Clifton et al., 2020; McDuie-Ra and Gulson, 2020). In G7 countries, for example, many employers hope that AI's incorporation into workplaces can induce higher rates of labor productivity (Clifton et al., 2020), while other parts of the world worry that the Fourth Industrial Revolution with which AI is associated will skip them entirely. As Clifton et al. (2020) recently noted, “the geography of technology development versus adoption appears to be following mostly tried and true paths based on existing sites of human capital formation and longstanding industrial concentrations” (p. 16). Under this arrangement, disruptive technologies like AI are enacted through the uneven geographies associated with previous waves of technological adoption to produce “technological islands” (UNIDO, 2020, 8), particularly within developing countries.

In their discussion of the “backroads of AI” that surround such “technological islands,” McDuie-Ra and Gulson (2020) describe this geography of uneven AI development: “AI is developed in the Global North and in the technology hubs of East Asia. The rest of the world is minimally involved in its development but will live with its disruptions as fragments of labour that go into its design and programming” (p. 627).⁶ In similar fashion, Lin (2022) highlights a new point of struggle in labor dynamics:

Whereas the contention before was between mobile capital and immobile labour, a new class tension is now emerging between the technoscientific elite and the infrastructure worker who is now not only at risk of redundancy but is also encouraged to retrain and reinvent themselves to avoid becoming obsolescent. (p. 474)

An equally important, if also open, question concerns AI's implications for labor struggles (Clifton et al., 2020). As a starting point, AI holds the potential to shape not only the nature of work but also the workforce itself, through its role in hiring decisions (Ajunwa, 2020). More broadly, as Ajunwa (2020) notes, AI is also shaping the labor process, as “the degree of control afforded by increased data collection creates the hazard of a ‘mission creep’ attitude to data collection that is detrimental to both the personhood and autonomy of workers” (1–2). Rutherford and Frangi (2020) examine this question of worker control and autonomy in the Canadian auto industry and find that while union workers could not stop workplace changes associated with the adoption of new technologies, they, via shop-floor representatives, could and did shape the paths that those technologies took. Additionally, there are lingering questions about “how the platformisation of (infra)structures is reshaping social relations and how labour could respond to platforms’ hydra-like reach,” with significant impacts on workers’ collective agency and “liberation” (Cole, 2023, 18). Such questions often coalesce around worker agency and surveillance (Wells et al., 2023), community activism, and the individualized self-produced through and envisioned by AI-driven applications like care robots (Pratt et al., 2023).

Surveillance

Because AI and its applications sort society and reproduce forms of control in a range of contexts, a significant body of research on the implications of AI and surveillance has emerged, stretching from the mundane practices of everyday digital tracking routines (e.g. Pink et al., 2017) to drone warfare (e.g. Crampton, 2015; Shaw and Akhter, 2014) to the surveillance of border fortification practices (e.g. McStay, 2020; Sánchez-Monedero and Dencik, 2022) to the dangers of facial recognition software, predictive policing, and other uses. In fact, it is difficult to overstate the degree to which contemporary surveillance is shaped by AI. As Clifton et al. (2020) note, while the private sector may be leading the development of AI technologies, the “largest consumers” of such technologies are likely to be “nation states, interested in defence and surveillance” (p. 20). Critical geographers have been instrumental in reflecting on the contours of such surveillance (e.g. Lynch and Del Casino, 2019; McDuie-Ra and Gulson, 2020), as have scholars in ethnic studies (e.g. De Lara, 2022) and those at the intersection of critical data studies and science and technology studies (e.g. Benjamin, 2020; Hamilton, 2020). Emerging from these fields is a consensus that data-based profiling reliant on aspects of AI “is teleologically linked to tracing, and, in turn, to different modes of repression of the surveilled” (Masiero, 2023, 1), particularly racialized and minoritized communities. Masiero argues that the architecture of digital identity systems enables their surveillance outcomes—what she terms a “platform view”—and suggests that surveillance is a feature built into the machinery (see also Murakami Wood and Monahan, 2019). How those on the receiving end of AI technologies are profiled and excluded is an increasingly well-covered topic (e.g. Akbari and Gabdulhakov, 2019; Martin and Taylor, 2021), and this research serves as a reminder that AI is implicated in sociospatial processes of adverse incorporation with uneven geographies.

Amidst the breadth and rapid growth of AI scholarship is a burgeoning focus on how technologies associated with AI monitor practices of life (e.g. Linder, 2019) and what new possibilities AI presents for governing. Such algorithmic governance “relies heavily on the construction of non-modern ontologies in which the world appears through processes of emergence” (Chandler, 2019, 1). From this view, correlation is a “more reliable and more objective ‘empirical’ method than the extrapolations and predictions of causal analysis” (ibid.), echoing Amoore and Piotukh's assertion that the “process generates the rules” (2015: 360). From air transportation security algorithms to the pair of shoes that follow you around on the internet until you purchase them, algorithmic systems perceive, reassemble, and document what may otherwise be imperceptible, disciplining subjects and rendering them visible and calculable (e.g. Prince, 2020; Lupton, 2016).

A robust and related body of research exists in the growing field of data justice (e.g. Noble, 2018; Dencik et al., 2019). Through “surveillance capitalism” (Zuboff, 2019), the human experience is transformed into raw material for Big Tech. Now, our networked lives are captured at every turn. Under the auspices of knowledge, data are claimed for the marketplace, and digital intermediaries intervene in ways that are not always apparent. Organizations design protocols, search engines index data, and algorithms sort according to invisible metrics (Suzor, 2019). Smart phones and the apps installed on them generate data as they move through space, coming near GPS points and joining the same Wi-Fi networks. The inner workings of digital infrastructure not only shape human behavior but also are concentrated in the hands of a few actors. In this way, AI is fast becoming a landscape of haves and have-nots, surveilling and surveilled, dominated by select companies who can outspend mid-size firms and even universities (Lohr, 2019).

Geographers have intervened extensively on the subject of environmental monitoring and surveillance (e.g. Lambach, 2022; Lehman, 2018) carried out via autonomous systems, drones, and mobile robotic platforms. Lehman (2016), for example, argues that technologies like the Global Ocean Observing System bring the ocean into a Deleuzian society of control, ushering in new understandings of the spatial and temporal constitutive elements of the non-human. Under the rubric of “climate AI,” Nost and Colven (2022) chart how technocratic approaches to climate crises catch the eye of philanthropies, non-governmental organizations, private consultancies, and tech giants precisely because of their surveillance potential (along with prospects for algorithmic commodification). As they argue, because AI approaches increasingly shape investment landscapes, decisions based on AI's perceived objectivity (i.e. climate risk ratings and who is vulnerable) are imbued with bias, effectively exacerbating inequalities within marginalized and minoritized communities. The related area of digital conservation and conservation by algorithm (Adams, 2017) reflects alignments between and among AI, machine learning, and the management of endangered wildlife and habitats. These technologies are surveillant in their ability to collect and transmit data, as well as in their claims to comprehensively know nature (e.g. Gabrys, 2020). The processes through which emerging technologies continue to embed valuations of nature and humans emphasize AI's entanglement with structures of colonialism and racial capitalism (e.g. Vera et al., 2019; Hughes et al., 2019).

Advances in sensing technology and robotic system design have been interrogated by geographers, especially vis-à-vis what these technologies mean for discipline, policing, and security (e.g. Shaw, 2017), as well as wider geopolitics in an era of AI. Robots and their algorithms have most visibly become perpetrators of state surveillance through attempts to create AI networks at the limits of sovereign space. Boyce (2016) writes about such efforts at the US–Mexico border, while Pero and Smith (2014) consider robotic technologies in Canada's “Temporary Resident Biometrics Project,” interrogating the use of facial recognition to manage mobility regimes through a geopolitics of exclusion. As our life-worlds become increasingly mediated through AI technologies, so, too, should possibilities to disrupt AI's surveillant capacities.

Activism

Artificial intelligence's implications for and imbrications with labor and surveillance generate a seemingly endless list of possibilities that can and should spur activism: the specter of job loss for large numbers of workers; the erosion or disappearance of worker autonomy and control of the labor process; the use of AI technologies to monitor marginalized and minoritized communities, populations, and spaces; and the reliance on AI to make decisions of life and death in law, healthcare, policing, and other domains. Nonetheless, within geography, there is less empirical work on activism prompted by AI than theoretical engagements with AI itself. In this penultimate section of our article, we highlight those geographers focusing on the transgressive or resistive possibilities of AI and the activisms associated with its applications.

Louise Amoore (2019) has grappled perhaps most directly with the question of how to critically engage AI and its manifestations, calling for sites of activism, or transgressive possibility, within algorithms. In doing so, she encourages geographers not only to engage “the political traces of machine learning algorithms” but also to question them and “listen to the doubt they themselves express in the world” (p. 162). As Amoore stresses, those doubts are also our own doubts, since “algorithms dwell within us, just as we too dwell as data and test subjects within their layers, so that we could not stand apart from this science even if we wanted to” (p. 163). In similar fashion, Del Casino et al. (2020) ask, “how [via AI] the spaces and architectures of policing, surveillance, and securitisation will be transformed and what potentials exist, or newly emerge, for resistance and subversion?” (p. 4). Echoing black feminists’ engagement with emerging technologies, they, like Amoore, urge us to seek resistive opportunities within AI systems and “to consider technical glitches and slippages, where the unexpected and unanticipated arises, and the potentials positive and negative that they enable” (p. 5). Lin et al. (2022) point to the ways that the human–machine teaming so central to AI creates “the fragile state of human–machine relations that can vacillate between different states of acceptance and resistance” (p. 5, see also Janowicz et al., 2022).

Focused more on the language and framework of the digital than AI per se, Nancy Ettlinger (2018) made an early call for thinking about resistance within and through emerging technologies, pointing to the lack of attention to digital resistance in theoretical engagement with digital governance. Through a discussion of Foucault's scholarship on ethics and resistance, she laid out “what we might call ‘algorithmic resistance’ in reference to productive use of elements of the digital environment such as algorithms, which can be used to resist various problems posed to digital subjects” (p. 4). For Ettlinger, “productive digital resistance” became algorithmic when digital subjects could “develop new elements of the digital environment (e.g. apps, software, websites…) that target and subvert strategies—technologies—of repressive power” (2018, 5), through practices like hacking.

Returning to McDuie-Ra and Gulson's work on the “backroads of AI,” we can see the ways that the geographies of AI development themselves point to potential geographies of AI resistance, particularly among workers: “As AI develops in concentrated geographies around tech-hubs, success will likely be measured and celebrated in these sites, while the casualties of labour force transformation in both the primary and ancillary workforce will be along the backroads, far from view” (2020, 631). Here, activism can emerge through the very geographies of AI development and adoption, particularly its impacts on workers and worker surveillance.

Aspects of AI activism and resistance have recently consolidated in a consensus in research and policy-making communities under the rubric of “ethical AI,” although largely outside geography (Jobin et al., 2019). A guiding principle of ethical AI is that “no technology, no matter how charming or clever, can accomplish the difficult, generational work of building a more just world” (Crooks, 2022, 20). As a result, ethical AI's motivation is to uncover biases and injustice associated with emerging technologies (Hagendorff, 2022). The impulse toward justice here includes strategies to incorporate minoritized communities in the production of technology, as well as public impact statements on the use of such technologies (Greene, 2021). Although discussions around the components of ethical AI are ongoing, scholars find that current proposals and guidelines fall short, tending toward the remedial rather than the transformative (Fukuda-Parr and Gibbons, 2021).

Conclusion

human geographers may not understand how little reception there is for some of the critiques that they/we make. (Janowicz et al., 2022, 457)

future AI, ML [machine learning], and deep learning research needs to be designed and implemented by people who understand the questions being asked, not just those who comprehend the models. (Lavallin and Downs, 2021, 11)

How can critical geographers effectively engage with AI's impacts on the world around us? Can we do so without training in computer science, coding, or machine learning? Will our critiques matter? Can we shape how AI is used (and not used) to create a more just world? These are large questions with which our discipline will grapple for the foreseeable future. To conclude this short article, we return to the simple argument we laid out in our introduction—critical geographers can, and must, grapple with these complex questions about AI's integration into our collective daily lives, and we must do so across our discipline. Simply put, geography's engagement with AI cannot be limited to those who “comprehend the models” or to those who “understand the questions being asked.” Instead, all parts of our discipline must turn a critical eye to the complicated geographies of AI in the world around us and bring a multi-faceted framework to discussions of this disruptive technology. Below, we identify some ways to begin this work.

First, and as multiple scholars have suggested, critical engagement with the geographies of AI must be deeply interdisciplinary, in the sense of bringing together and drawing on knowledge from multiple disciplines and perspectives. For the same reasons that one subdiscipline in geography cannot be wholly responsible for our discipline's contributions to understandings of AI, one discipline alone cannot help us better understand AI and its effects. Thus, geographers must not only engage with one another across subfields but also engage with a wide range of other fields—computer science, information studies, software studies, policy, design, philosophy, and so on—in their work on AI.

Second, engaging critically with AI will require new forms of methodological and theoretical training for many geographers, ourselves included. We came to the topic of AI along circuitous routes that thread through urban and cultural geography, border studies, migration studies, digital geographies, and other fields. For scholars, including us, to work across subdisciplinary and disciplinary lines, we all must become more familiar with methods, theories, and frameworks in disciplines not always linked directly to human geography. In the same ways that the subfield of creative geographies pushes geographers to go beyond writing about creative endeavors and to partner with artists, if not ourselves produce creative material, a critical geography of AI requires that many of us move out of our methodological and epistemological comfort zones to partner with scholars in other fields and creators, designers, and users of these emerging technologies.

To date, writing on AI in geography has tended toward summarizing and synthesizing, and while we, in this article and elsewhere (self-reference), are complicit with this problem, critical geographic engagement with AI needs more empirically driven scholarship. We need research on the detailed mechanisms through which AI configures and re-configures our life-worlds if we are to understand when, where, how, and to what effect these technologies are transformative. How AI is constitutive of and realized in and through space and how spaces develop differently as a result of these technologies remain pressing questions across geography and ones that are best approached across subdisciplines.

Finally, and perhaps most importantly, if we collectively are invested in ensuring a just world in the face of AI adoption, we must bring a critical and interdisciplinary perspective on AI into our classrooms. Writing about the geographies of AI, as we do here, is an important first step in moving geography (and other disciplines) toward more critical engagement with the topic. This article and the research it summarizes, however, will have far greater reach if and when we bring critical perspectives on AI into our lectures, seminars, laboratories, and studios and to our students, graduate and undergraduate.

Footnotes

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Notes

Author Biographies

Margath Walker is a professor of geography at the University of Louisville. Her research focuses on borders, political geography and critical theory.

Jamie L Winders is a professor in the Department of Geography and the Environment at Syracuse University.

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