Louise Amoore

This essay explores the changing form of the digital twin as a political technology in the age of deep learning and generative artificial intelligence (AI). It situates the digital twin as a distinctive contemporary form of simulation and examines how it is making up people, things, scenes and their interactions in novel ways: the drawing in of unstructured datastreams and their experimental recombining and modification. Although the digital twin is often represented as a digital copy or replica of the physical world, it actually departs radically from a mimetic copy; advancing a mode of twinning that classifies, extracts and divides. The essay addresses digital twinning as an emerging form of knowledge and action in the world. It proposes three distinctive logics of the contemporary politics of digital twins: intervention, prediction and action. Each of these aspects is discussed through the situated digital twin domains of the factory, the clinic and the battlefield.

Computer science tends to foreclose the reading of its texts by social science and humanities scholars – via code and scale, mathematics, black box opacities, secret or proprietary models. Yet, when computer science papers are read in order to better understand what machine learning means for societies, a form of reading is brought to bear that is not primarily about excavating the hidden meaning of a text or exposing underlying truths about science. Not strictly reading to make sense or to discern definitive meaning of computer science texts, reading is an engagement with the sense-making and meaning-making that takes place. We propose a strategy for reading computer science that is attentive to the act of reading itself, that stays close to the difficulty involved in all forms of reading, and that works with the text as already properly belonging to the ethico-politics that this difficulty engenders. Addressing a series of three “reading problems” – genre, readability, and meaning – we discuss machine learning textbooks and papers as sites where today's algorithmic models are actively giving accounts of their paradigmatic worldview. Much more than matters of technical definition or proof of concept, texts are sites where concepts are forged and contested. In our times, when the political application of AI and machine learning is so commonly geared to settle or predict difficult societal problems in advance, a reading strategy must open the gaps and difficulties of that which cannot be settled or resolved.

Policy discussions and corporate strategies on machine learning are increasingly championing data reuse as a key element in digital transformations. These aspirations are often coupled with a focus on responsibility, ethics and transparency, as well as emergent forms of regulation that seek to set demands for corporate conduct and the protection of civic rights. And the Protective measures include methods of traceability and assessments of ‘good’ and ‘bad’ datasets and algorithms that are considered to be traceable, stable and contained. However, these ways of thinking about both technology and ethics obscure a fundamental issue, namely that machine learning systems entangle data, algorithms and more-than-human environments in ways that challenge a well-defined separation. This article investigates the fundamental fallacy of most data reuse strategies as well as their regulation and mitigation strategies that data can somehow be followed, contained and controlled in machine learning processes. Instead, the article argues that we need to understand the reuse of data as an inherently entangled phenomenon. To examine this tension between the discursive regimes and the realities of data reuse, we advance the notion of reuse entanglements as an analytical lens. The main contribution of the article is the conceptualization of reuse that places entanglements at its core and the articulation of its relevance using empirical illustrations. This is important, we argue, for our understanding of the nature of data and algorithms, for the practical uses of data and algorithms and our attitudes regarding ethics, responsibility and regulation.

A significant set of epistemic and political transformations are taking place as states and societies begin to understand themselves and their problems through the paradigm of deep neural network algorithms. A machine learning political order does not merely change the political technologies of governance, but is itself a reordering of politics, of what the political can be. When algorithmic systems reduce the pluridimensionality of politics to the output of a model, they simultaneously foreclose the potential for other political claims to be made and alternative political projects to be built. More than this foreclosure, a machine learning political order actively profits and learns from the fracturing of communities and the destabilising of democratic rights. The transformation from rules-based algorithms to deep learning models has paralleled the undoing of rules-based social and international orders – from the use of machine learning in the campaigns of the UK EU referendum, to the trialling of algorithmic immigration and welfare systems, and the use of deep learning in the COVID-19 pandemic – with political problems becoming reconfigured as machine learning problems. Machine learning political orders decouple their attributes, features and clusters from underlying social values, no longer tethered to notions of good governance or a good society, but searching instead for the optimal function of abstract representations of data.

At the time of writing in spring 2021, Guantanamo Bay continues to hold the last forty prisoners who were captured and held without trial at the US Naval Base in Cuba since the US invasion in Afgha...