As investment in Generative AI (GenAI) has reached tens and hundreds of billions, anxieties about an ‘AI bubble’ have been on the rise. This article offers an alternative perspective on how GenAI is made valuable. Drawing on Luc Boltanski and Arnaud Esquerre's economy of enrichment, it argues that benchmarks have become devices of GenAI valuation, which make Large Language Models (LLMs) into singular, exceptional and non-standard objects. Benchmarks enrich LLMs by mobilising epistemic cultures of science and narratives of a future perfect of Artificial General Intelligence (AGI). To develop this argument, we first show that the perspective of enrichment can supplement the frameworks of surveillance capitalism, platform capitalism, and assetisation by accounting for the centrality of benchmarks as devices that integrate models within a collection of elite models and simultaneously differentiate them as singularities. Second, instead of cultures of the past, GenAI narratives draw on epistemic cultures of science. With GenAI, the creation of new benchmarks has become a commercial pursuit, going beyond computer science and relying on various benchmarks from the ‘psy’ sciences to address the demands of hyper-scale commercial GenAI. Third, we unpack how narratives of saturation, surpassing, and emergence singularise models by situating them in proximity to a future perfect of AGI. In the conclusion, we offer reflections on the potential of the enrichment economy for critical engagement with GenAI.

As machine learning (ML) technologies move from their discrete existence in research to being highly applied technologies across society, critical scholars have begun to address the epistemological conditions that shape the emergence of such systems and their societal implications. In this paper, we investigate a specific epistemological condition of ML, namely, how ML systems rely on ongoing negotiations and agreements of ‘good enough’ to be deployed. We do so by drawing on ethnographic fieldwork with the British Broadcasting Corporation (BBC) – a large data- and value-driven organisation. In studying the epistemological function and politics of ‘good enough’, we take an AI lab studies approach, following the Recommendations Team's efforts to materialise ‘good enoughness’ and make it negotiable as they develop and modify recommender systems that aim to better serve the BBC's audiences. Through our ethnographic account, we demonstrate how the team relies on various metrics and qualitative evaluations to inform provisional performance thresholds before submitting the ML systems to A/B testing to establish whether one of them is ‘good enough’ to deploy. By following these processes of establishing ‘good enough’, we see how these negotiations are entangled in various, sometimes competing organisational objectives, as well as particular data and technical infrastructures. By extension, we show how the metricised performance scores of AB testing are negotiated in practice by readjusting performance thresholds to manoeuvre different values and constraints. Ultimately, our paper shows that establishing ‘good enough’ is a political endeavour of adjusting seemingly objective evaluation criteria to find the best-fitting metrics and ‘right’ thresholds.

Archives have long been a key concern of academic debates about truth, memory, recording and power and are important sites for social sciences and humanities research. This has been the case for traditional archives, but these debates have accelerated with the digital transformation of archives. The proliferation of digital tools and the fast-growing increase in digital materials have created very large digitised and born-digital archives. This article investigates how new digital archives continue existing archival practices while at the same time discontinuing them. We present novel methodologies and tools for changing memory and power relations in digital archives through new ways of reassembling marginalised, non-canonical entities in digital archives. Reassembling digital archives can take advantage of the materiality and the algorithmic processuality of digital collections and reshape them to inscribe lost voices and previously ignored differences. Digital archives are not fixed and are changed with new research and political questions and are only identified through new questions. The article presents six distinct techniques and strategies to reassemble digital archives and renders these according to three different types of new digital archives. We consider both the extension of archives towards evidence that is otherwise thrown away as well as the provision of new intensive, non-discriminatory viewpoints on existing collections.

Abstract This article brings debates about data visualization in digital humanities in conversation with critical security studies and international relations. Building on feminist approaches in digital humanities, we explore the potential and limitations of data visualization as a critical method for research on (in)security. We unpack three aspects of making data visualizations by specifying “making” in this context as working, orienting, and critiquing. Making data visualizations as a methodological device is oriented by questions about the contestation of security and orients research by provoking new questions about practices of critique. Empirically, we situate data visualizations within British parliamentary debates about the Government Communications Headquarters (GCHQ), the UK's signals intelligence agency, which has garnered much public attention in the wake of the Snowden disclosures of transnational mass surveillance. We argue that data visualization in the parliamentary archive can destabilize dominant understandings of security, problematize narratives of security actors and oversight, and attend to the uneven presence of critique and contestation within and beyond parliamentary debates.

Algorithmic Reason: Tobias Blanke interviewed by Puspa Damai

AbstractAs facial recognition is increasingly deployed around the world, from the US to China, civil liberties activists and democratic actors have drawn attention to its error rates and privacy invasions. The chapter unpacks new facets of algorithmic accountability, as it emerged nationally and transnationally by producing accounts of algorithmic error and by providing trustworthy explanations of what algorithms do. An algorithmic accountability and auditing industry has emerged to answer growing concerns that humans cannot trust fast-developing algorithms. Rather than analysing accountability through techniques of verification and responsibilization, we draw on scenes of contestation of facial recognition in China to develop another form of calling to account through refusal. Attending to refusal as a form of accountability expands the political scene of algorithmic interventions and challenges how liberal and authoritarian imaginaries to technological innovation are allocated following geopolitical lines.

In 2020, the European Union announced the award of the contract for the biometric part of the new database for border control, the Entry Exit System, to two companies: IDEMIA and Sopra Steria. Both companies had been previously involved in the development of databases for border and migration management. While there has been a growing amount of publicly available documents that show what kind of technologies are being implemented, for how much money, and by whom, there has been limited engagement with digital methods in this field. Moreover, critical border and security scholarship has largely focused on qualitative and ethnographic methods. Building on a data feminist approach, we propose a transdisciplinary methodology that goes beyond binaries of qualitative/quantitative and opacity/transparency, examines power asymmetries and makes the labour of coding visible. Empirically, we build and analyse a dataset of the contracts awarded by two European Union agencies key to its border management policies – the European Agency for Large-Scale Information Systems (eu-LISA) and the European Border and Coast Guard Agency (Frontex). We supplement the digital analysis and visualisation of networks of companies with close reading of tender documents. In so doing, we show how a transdisciplinary methodology can be a device for making datafication ‘intelligible’ at the European Union borders.

Are algorithms ruling the world today? Is artificial intelligence making life-and-death decisions? Are social media companies able to manipulate elections? As we are confronted with public and academic anxieties about unprecedented changes, this book offers a different analytical prism to investigate these transformations as more mundane and fraught. Aradau and Blanke develop conceptual and methodological tools to understand how algorithmic operations shape the government of self and other. While disperse and messy, these operations are held together by an ascendant algorithmic reason. Through a global perspective on algorithmic operations, the book helps us understand how algorithmic reason redraws boundaries and reconfigures differences. The book explores the emergence of algorithmic reason through rationalities, materializations, and interventions. It traces how algorithmic rationalities of decomposition, recomposition, and partitioning are materialized in the construction of dangerous others, the power of platforms, and the production of economic value. The book shows how political interventions to make algorithms governable encounter friction, refusal, and resistance. The theoretical perspective on algorithmic reason is developed through qualitative and digital methods to investigate scenes and controversies that range from mass surveillance and the Cambridge Analytica scandal in the UK to predictive policing in the US, and from the use of facial recognition in China and drone targeting in Pakistan to the regulation of hate speech in Germany. Algorithmic Reason offers an alternative to dystopia and despair through a transdisciplinary approach made possible by the authors’ backgrounds, which span the humanities, social sciences, and computer sciences.

AbstractHow do algorithms make decisions, how do they draw lines of difference? Mobilizing the lesser-known critical theory of Günther Anders, this chapter argues that we need to approach algorithmic decision-making through the prism of production and distributed human-machine work. To this end, we develop a methodology to ‘follow an algorithm’ marketed by CivicScape, a predictive policing company. Algorithmic decisions emerge via geometrical calculations and the spatialized partitioning of data points. These decisions become difficult to trace, given what Anders calls their infra-sensible and supra-sensible character. By situating our analysis within the scene of predictive policing, we show that a new rationality of partitioning is constitutive of algorithmic reason and the government of difference.

There is a bidirectional relationship between culture and AI; AI models are increasingly used to analyse culture, thereby shaping our understanding of culture. On the other hand, the models are trained on collections of cultural artifacts thereby implicitly, and not always correctly, encoding expressions of culture. This creates a tension that both limits the use of AI for analysing culture and leads to problems in AI with respect to cultural complex issues such as bias. One approach to overcome this tension is to more extensively take into account the intricacies and complexities of culture. We structure our discussion using four concepts that guide humanistic inquiry into culture: subjectivity, scalability, contextuality, and temporality. We focus on these concepts because they have not yet been sufficiently represented in AI research. We believe that possible implementations of these aspects into AI research leads to AI that better captures the complexities of culture. In what follows, we briefly describe these four concepts and their absence in AI research. For each concept, we define possible research challenges.

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The digital transformation is turning archives, both old and new, into data. As a consequence, automation in the form of artificial intelligence techniques is increasingly applied both to scale traditional recordkeeping activities, and to experiment with novel ways to capture, organise, and access records. We survey recent developments at the intersection of Artificial Intelligence and archival thinking and practice. Our overview of this growing body of literature is organised through the lenses of the Records Continuum model. We find four broad themes in the literature on archives and artificial intelligence: theoretical and professional considerations, the automation of recordkeeping processes, organising and accessing archives, and novel forms of digital archives. We conclude by underlining emerging trends and directions for future work, which include the application of recordkeeping principles to the very data and processes that power modern artificial intelligence and a more structural—yet critically aware—integration of artificial intelligence into archival systems and practice.

The digital transformation is turning archives, both old and new, into data. As a consequence, automation in the form of artificial intelligence techniques is increasingly applied both to scale traditional recordkeeping activities, and to experiment with novel ways to capture, organise, and access records. We survey recent developments at the intersection of Artificial Intelligence and archival thinking and practice. Our overview of this growing body of literature is organised through the lenses of the Records Continuum model. We find four broad themes in the literature on archives and artificial intelligence: theoretical and professional considerations, the automation of recordkeeping processes, organising and accessing archives, and novel forms of digital archives. We conclude by underlining emerging trends and directions for future work, which include the application of recordkeeping principles to the very data and processes that power modern artificial intelligence and a more structural—yet critically aware—integration of artificial intelligence into archival systems and practice.

Concerns with errors, mistakes, and inaccuracies have shaped political debates about what technologies do, where and how certain technologies can be used, and for which purposes. However, error has received scant attention in the emerging field of ignorance studies. In this article, we analyze how errors have been mobilized in scientific and public controversies over surveillance technologies. In juxtaposing nineteenth-century debates about the errors of biometric technologies for policing and surveillance to current criticisms of facial recognition systems, we trace a transformation of error and its political life. We argue that the modern preoccupation with error and the intellectual habits inculcated to eliminate or tame it have been transformed with machine learning. Machine learning algorithms do not eliminate or tame error, but they optimize it. Therefore, despite reports by digital rights activists, civil liberties organizations, and academics highlighting algorithmic bias and error, facial recognition systems have continued to be rolled out. Drawing on a landmark legal case around facial recognition in the UK, we show how optimizing error also remakes the conditions for a critique of surveillance. This article is part of a special issue entitled “Histories of Ignorance,” edited by Lukas M. Verburgt and Peter Burke.

Articulations of discontinuity and moments of dissent have been central to critical historical work. However, such vocabularies and analyses of historical change have received less attention in the emerging field of digital methods. Digital methods based on discerning patterns have focused on continuities, while discontinuities and ruptures have been derivative of trends and patterns. By contrast, genealogical methods attend to the entanglement of continuity and discontinuity, and focus on contingency and singularity. This article proposes to develop methods of computational genealogy to analyze multiple temporalities in historical discourses. We experiment with our proposed computational genealogy using the archive of Inaugural speeches by US presidents. In particular, we show that there is neither a linear advance to Trump's rhetoric nor an exceptional rupture. Our analysis shows that Trump's speech is much more the struggle of the Republicans with their own past ideas than struggles with Democrats.