Artificial Intelligence-as-a-Service empowers individuals and organisations to access AI on-demand, in either tailored or ‘off-the-shelf’ forms. However, institutional separation between development, training and deployment can lead to critical opacities, such as obscuring the level of human effort necessary to produce and train AI services. Information about how, where, and for whom AI services have been produced are valuable secrets, which vendors strategically disclose to clients depending on commercial interests. This article provides a critical analysis of how AIaaS vendors manipulate the (...) visibility of human labour in AI production based on whether the vendor relies on paid or unpaid labour to fill interstitial gaps. Where vendors are able to occlude human labour in the organisational ‘backstage,’ such as in data preparation, validation or impersonation, they do so regularly, further contributing to ongoing techno-utopian narratives of AI hype. Yet, when vendors must co-produce the AI service with the client, such as through localised AI training, they must ‘lift the curtain’, resulting in a paradoxical situation of needing to both perpetuate dominant AI hype narratives while emphasising AI’s mundane limitations. (shrink)

We are repeatedly told that AI will help us to solve some of the world's biggest challenges, from treating chronic diseases and reducing fatality rates in traffic accidents to fighting climate change and anticipating cybersecurity threats. However, the article contends that public discourse on AI systematically avoids considering AI’s environmental costs. Artificial Intelligence- Brevini argues- runs on technology, machines, and infrastructures that deplete scarce resources in their production, consumption, and disposal, thus increasing the amounts of energy in their use, and (...) exacerbate problems of waste and pollution. It also relies on data centers, that demands impressive amounts of energy to compute, analyse, categorize. If we want to stand a chance at tackling the Climate Emergency, then we have to stop avoiding addressing the environmental problems generated by AI. (shrink)

This article responds to recent debates in critical algorithm studies about the significance of the term “algorithm.” Where some have suggested that critical scholars should align their use of the term with its common definition in professional computer science, I argue that we should instead approach algorithms as “multiples”—unstable objects that are enacted through the varied practices that people use to engage with them, including the practices of “outsider” researchers. This approach builds on the work of Laura Devendorf, Elizabeth Goodman, (...) and Annemarie Mol. Different ways of enacting algorithms foreground certain issues while occluding others: computer scientists enact algorithms as conceptual objects indifferent to implementation details, while calls for accountability enact algorithms as closed boxes to be opened. I propose that critical researchers might seek to enact algorithms ethnographically, seeing them as heterogeneous and diffuse sociotechnical systems, rather than rigidly constrained and procedural formulas. To do so, I suggest thinking of algorithms not “in” culture, as the event occasioning this essay was titled, but “as” culture: part of broad patterns of meaning and practice that can be engaged with empirically. I offer a set of practical tactics for the ethnographic enactment of algorithmic systems, which do not depend on pinning down a singular “algorithm” or achieving “access,” but which rather work from the partial and mobile position of an outsider. (shrink)

Artificial Intelligence in the form of different machine learning models is applied to Big Data as a way to turn data into valuable knowledge. The rhetoric is that ensuing predictions work well—with a high degree of autonomy and automation. We argue that we need to analyze the process of applying machine learning in depth and highlight at what point human knowledge production takes place in seemingly autonomous work. This article reintroduces classification theory as an important framework for understanding such seemingly (...) invisible knowledge production in the machine learning development and design processes. We suggest a framework for studying such classification closely tied to different steps in the work process and exemplify the framework on two experiments with machine learning applied to Facebook data from one of our labs. By doing so we demonstrate ways in which classification and potential discrimination take place in even seemingly unsupervised and autonomous models. Moving away from concepts of non-supervision and autonomy enable us to understand the underlying classificatory dispositifs in the work process and that this form of analysis constitutes a first step towards governance of artificial intelligence. (shrink)

This paper sheds light on the role of digital platform labour in the development of today’s artificial intelligence, predicated on data-intensive machine learning algorithms. Focus is on the specific ways in which outsourcing of data tasks to myriad ‘micro-workers’, recruited and managed through specialized platforms, powers virtual assistants, self-driving vehicles and connected objects. Using qualitative data from multiple sources, we show that micro-work performs a variety of functions, between three poles that we label, respectively, ‘artificial intelligence preparation’, ‘artificial intelligence verification’ (...) and ‘artificial intelligence impersonation’. Because of the wide scope of application of micro-work, it is a structural component of contemporary artificial intelligence production processes – not an ephemeral form of support that may vanish once the technology reaches maturity stage. Through the lens of micro-work, we prefigure the policy implications of a future in which data technologies do not replace human workforce but imply its marginalization and precariousness. (shrink)

Some enlightenment regarding the project to mechanise reason. The assembly line of machine learning: data, algorithm, model. The training dataset: the social origins of machine intelligence. The history of AI as the automation of perception. The learning algorithm: compressing the world into a statistical model. All models are wrong, but some are useful. World to vector: the society of classification and prediction bots. Faults of a statistical instrument: the undetection of the new. Adversarial intelligence vs. statistical intelligence: labour in the (...) age of AI. (shrink)

This paper focuses on the fact that AI is predominantly portrayed as white—in colour, ethnicity, or both. We first illustrate the prevalent Whiteness of real and imagined intelligent machines in four categories: humanoid robots, chatbots and virtual assistants, stock images of AI, and portrayals of AI in film and television. We then offer three interpretations of the Whiteness of AI, drawing on critical race theory, particularly the idea of the White racial frame. First, we examine the extent to which this (...) Whiteness might simply reflect the predominantly White milieus from which these artefacts arise. Second, we argue that to imagine machines that are intelligent, professional, or powerful is to imagine White machines because the White racial frame ascribes these attributes predominantly to White people. Third, we argue that AI racialised as White allows for a full erasure of people of colour from the White utopian imaginary. Finally, we examine potential consequences of the racialisation of AI, arguing it could exacerbate bias and misdirect concern. (shrink)

This paper explores the important role of critical science, and in particular of post-colonial and decolonial theories, in understanding and shaping the ongoing advances in artificial intelligence. Artificial intelligence is viewed as amongst the technological advances that will reshape modern societies and their relations. While the design and deployment of systems that continually adapt holds the promise of far-reaching positive change, they simultaneously pose significant risks, especially to already vulnerable peoples. Values and power are central to this discussion. Decolonial theories (...) use historical hindsight to explain patterns of power that shape our intellectual, political, economic, and social world. By embedding a decolonial critical approach within its technical practice, AI communities can develop foresight and tactics that can better align research and technology development with established ethical principles, centring vulnerable peoples who continue to bear the brunt of negative impacts of innovation and scientific progress. We highlight problematic applications that are instances of coloniality, and using a decolonial lens, submit three tactics that can form a decolonial field of artificial intelligence: creating a critical technical practice of AI, seeking reverse tutelage and reverse pedagogies, and the renewal of affective and political communities. The years ahead will usher in a wave of new scientific breakthroughs and technologies driven by AI research, making it incumbent upon AI communities to strengthen the social contract through ethical foresight and the multiplicity of intellectual perspectives available to us, ultimately supporting future technologies that enable greater well-being, with the goal of beneficence and justice for all. (shrink)

In information societies, operations, decisions and choices previously left to humans are increasingly delegated to algorithms, which may advise, if not decide, about how data should be interpreted and what actions should be taken as a result. More and more often, algorithms mediate social processes, business transactions, governmental decisions, and how we perceive, understand, and interact among ourselves and with the environment. Gaps between the design and operation of algorithms and our understanding of their ethical implications can have severe consequences (...) affecting individuals as well as groups and whole societies. This paper makes three contributions to clarify the ethical importance of algorithmic mediation. It provides a prescriptive map to organise the debate. It reviews the current discussion of ethical aspects of algorithms. And it assesses the available literature in order to identify areas requiring further work to develop the ethics of algorithms. (shrink)

The decolonial theory understands that Western Modernity keeps imposing itself through a triple mutually reinforcing and shaping imprisonment: coloniality of power, coloniality of knowledge, and coloniality of being. Technical design has an essential role in either maintaining or overcoming coloniality. In this article, two main approaches to decolonizing the technical design are presented. First is Yuk Hui’s and Ahmed Ansari’s proposals that, revisiting or recovering the different histories and philosophies of technology produced by humankind, intend to decolonize the minds of (...) philosophers and engineers/architects/designers as a pre-condition for such decolonial designs to take place. I call them top-down approaches. Second is some technical design initiatives that, being developed alongside marginalized/subalternate people, intend to co-construct decolonial sociotechnical solutions through a committed, decolonizing, and careful dialog of knowledge. I call them bottom-up approaches. Once that is done, the article’s second half derives ontological, epistemological, and political consequences from the conjugation of top-down and bottom-up approaches. Such consequences challenge some established or not yet entirely overcome understandings in the philosophy of technology and, in so doing, are meant to represent some steps in PT’s decolonization. Even though both top-down and bottom-up approaches are considered, the article’s main contributions are associated with decolonial technical design practices, whose methodologies and outcomes are important study cases for PT and whose practitioners can be taken as inspiring examples for philosophers who want to decolonize/enlarge PT or make it decolonial. (shrink)

To a greater extent than in other technical domains, research and progress in Artificial Intelligence has always been entwined with the fictional. Its language echoes strongly with other forms of cultural narratives, such as fairytales, myth and religion. In this essay we present varied examples that illustrate how these analogies have guided not only readings of the AI enterprise by commentators outside the community but also inspired AI researchers themselves. Owing to their influence, we pay particular attention to the similarities (...) between religious language and the way in which the potential advent of greater than human intelligence is presented contemporarily. We then move on to the role that fiction, science fiction most of all, has historically played and is still playing in the discussion of AI by influencing researchers and the public, shifting the weights of different scenarios in our collectively perceived probability space. We sum up by arguing that the lore surrounding AI research, ancient and modern, points to the ancestral and shared human motivations that drive researchers in their pursuit and fascinate humanity at large. These points of narrative entanglement where AI meets the wider culture should serve to amplify the call to engage ourselves with the discussion of the potential destination of this technology. (shrink)

Through case studies that highlight the type of information that is seldom reported in the news, Faces of Environmental Racism exposes the type and magnitude of environmental racism, both domestic and international. The essays explore the justice of current environmental practices, asking such questions as whether cost-benefit analysis is an appropriate analytic technique and whether there are alternate routes to sustainable development in the South.