The aim of this exploratory paper is to review an under-appreciated parallel between group agency and artificial intelligence. As both phenomena involve non-human goal-directed agents that can make a difference to the social world, they raise some similar moral and regulatory challenges, which require us to rethink some of our anthropocentric moral assumptions. Are humans always responsible for those entities’ actions, or could the entities bear responsibility themselves? Could the entities engage in normative reasoning? Could they even have rights and (...) a moral status? I will tentatively defend the (increasingly widely held) view that, under certain conditions, artificial intelligent systems, like corporate entities, might qualify as responsible moral agents and as holders of limited rights and legal personhood. I will further suggest that regulators should permit the use of autonomous artificial systems in high-stakes settings only if they are engineered to function as moral (not just intentional) agents and/or there is some liability-transfer arrangement in place. I will finally raise the possibility that if artificial systems ever became phenomenally conscious, there might be a case for extending a stronger moral status to them, but argue that, as of now, this remains very hypothetical. (shrink)

The Covid-19 pandemic broke on a world whose grip on epistemic trust was already in disarray. The first months of the pandemic saw many governments publicly performing reliance on epidemiological and modelling expertise in order to signal that data would be the basis for justifying whatever population-level measures of control were judged necessary. But comprehensive data has not become available, and instead scientists, policymakers and the public find themselves in a situation where policy inputs determine the data available and vice (...) versa. This essay asks how we can live with what Amoore has termed ‘post-Cartesian doubt’ in situations of existential risk, and what kind of approach to science and data can answer the moral and human demands of a situation such as the Covid-19 pandemic. I suggest that science and policy could be able to control the pandemic better by addressing the sources of uncertainty and missing data not as gaps in the information landscape, but as individuals who are likely to be members of less-visible and less powerful groups including low-wage workers, the elderly, migrants, prisoners and others. This would shift both data use and policy toward an ethics of care, an embodied approach which asks what people need and how they behave in relation to each other, rather than how to manage population-level behaviour. This approach, I argue, is more appropriate for pandemic response than a utilitarian calculation of how many people each country should expect to lose as a result of the disease. (shrink)

Algorithmic vision, the computational process of making meaning from digital images or visual information, has changed the relationship between the image and the human subject. In this paper, I explicate on the role of algorithmic vision as a technique of algorithmic governance, the organization of a population by algorithmic means. With its roots in the United States post-war cybernetic sciences, the ontological status of the computational image undergoes a shift, giving way to the hegemonic use of automated facial recognition technologies (...) towards predatory policing and profiling practices. By way of example, I argue that algorithmic vision reconfigures the philosophical links between vision, image, and truth, paradigmatically changing the way a human subject is represented through imagistic data. With algorithmic vision, the relationship between subject and representation challenges the humanistic discourse around images, calling for a critical displacement of the human subject from the center of an analysis of how computational images make meaning. I will explore the relationship between the operative image, the image that acts but is not seen by human eyes, and what Louise Amoore calls an “emergent subject,” a subject that is made visible through algorithmic techniques. Algorithmic vision reveals subjects to power in a mode that requires a new approach towards analyzing the entanglement and invisiblization of the human in automated decision-making systems. (shrink)

During 2018, as part of a research project funded by the Deviant Practice Grant, artist Bruno Moreschi and digital media researcher Gabriel Pereira worked with the Van Abbemuseum collection (Eindhoven, NL), reading their artworks through commercial image-recognition (computer vision) artificial intelligences from leading tech companies. The main takeaways were: somewhat as expected, AI is constructed through a capitalist and product-focused reading of the world (values that are embedded in this sociotechnical system); and that this process of using AI is an (...) innovative way for doing institutional critique, as AI offers an untrained eye that reveals the inner workings of the art system through its glitches. This paper aims to regard these glitches as potentially revealing of the art system, and even poetic at times. We also look at them as a way of revealing the inherent fallibility of the commercial use of AI and machine learning to catalogue the world: it cannot comprehend other ways of knowing about the world, outside the logic of the algorithm. But, at the same time, due to their “glitchy” capacity to level and reimagine, these faulty readings can also serve as a new way of reading art; a new way for thinking critically about the art image in a moment when visual culture has changed form to hybrids of human–machine cognition and “machine-to-machine seeing”. (shrink)

What happens when there is not enough data to train machine learning algorithms? In recent years, so-called ‘synthetic data’ have been increasingly used to add to or supplement the training regimes of various machine learning algorithms. Seeking to read the notion of supplementarity differently through an engagement with the work of Jacques Derrida, I propose that the nascent emergence of synthetic data embodies what I call the logic of the synthetic supplement in algorithmic societies. I argue, on the one hand, (...) that the synthetic supplement promises and claims to resolve the ethico-political tensions, frictions, and intractabilities of machine learning. On the other hand, it always falls short of these promises because it necessarily intervenes in that which it claims to merely augment. Ultimately, this means that the gaps and frictions of machine learning cannot be completely filled, supplemented, or resolved. (shrink)

This paper centres the colonial pre-histories of ‘the digital’ to complicate posthumanist theorisations of subjectivity. Posthumanism helpfully undercuts human exceptionalism by presenting subjectivity as always-already co-constituted by technology. However, this paper argues that it insufficiently engages the human as the historico-political effect of negating the assumed non-technological colonial Other. Focusing on liberal humanism between the 16th and 19th centuries, the paper theorises the modern human as bound up in ‘technological onticide’. The presumed absence of technology became a (theo-centric, ratio-centric, bio-centric) (...) measure of the Other’s sub-humanity, at the same time as this Other was expected to be humanised through its technologisation. An emphasis on technological onticide complicates universalist theories of subjectivity that take it as always a matter of human-technology co-constitution. The paper argues that, to confront the legacies of ontological murder, conceptual room needs to be made for inhuman, counterhuman or unhuman theories of subjectivity. (shrink)

This article explores the impact of the COVID-19 pandemic on the relationship between bodies, risk and mobility. Drawing upon ethnographic data from India, it is argued that measures taken by the Indian government to contain the spread of the pandemic link mobile bodies to the notion of risk which has profound consequences for the way in which people access and engage with public spaces in Indian cities. In this process, a new type of body – the risky mobile body – (...) is produced. At the same time, these measures run into problems due to the volatile nature of knowledge about bodies and diseases that they rely on. While the mobility of the COVID-19 virus is a subject of public debate, the fluidity and open-endedness of mobile bodies makes them difficult to regulate. This mismatch between governmental logics and unknowable bodies constitutes a significant challenge for the fight against the pandemic. (shrink)

In the digital age, the use of advanced technology is becoming a new paradigm in police work, criminal justice, and the penal system. Algorithms promise to predict delinquent behaviour, identify potentially dangerous persons, and support crime investigation. Algorithm-based applications are often deployed in this context, laying the groundwork for a ‘smart criminal justice’. In this qualitative study based on 32 interviews with criminal justice and police officials, we explore the reasons why and extent to which such a smart criminal justice (...) system has already been established in Switzerland, and the benefits perceived by users. Drawing upon this research, we address the spread, application, technical background, institutional implementation, and psychological aspects of the use of algorithms in the criminal justice system. We find that the Swiss criminal justice system is already significantly shaped by algorithms, a change motivated by political expectations and demands for efficiency. Until now, algorithms have only been used at a low level of automation and technical complexity and the levels of benefit perceived vary. This study also identifies the need for critical evaluation and research-based optimization of the implementation of advanced technology. Societal implications, as well as the legal foundations of the use of algorithms, are often insufficiently taken into account. By discussing the main challenges to and issues with algorithm use in this field, this work lays the foundation for further research and debate regarding how to guarantee that ‘smart’ criminal justice is actually carried out smartly. (shrink)

From a Benjaminian point of view, AI-generated art is distinct from both ‘traditional’ art and technologically enabled reproduction, for example, photography and film. Instead of mere mechanical representation of the world as it is presented to a device, AI-generated art involves identification and inventive representation of data patterns. This specific mode of data-based generation exceeds mere surface-level mimicry and enables deeper meaning, namely, an insight into the collective unconscious of the society. In this way, AI-generated art is never detached from (...) society and the predominant social conditions while also reflecting the technology-induced transformations that today’s societies are undergoing. Thus, AI-generated art can be seen as capable of partly reversing the loss of auratic capacities that hand ensued with mechanical reproduction. Still, as a matter of continuity, AI-generated works enable the maximisation of exhibition value and capacity for audience enjoyment, rendering AI-generated art perfect for the age of increasing distraction. (shrink)

All datasets emerge from and are enmeshed in power-laden semiotic systems. While emerging data ethics curriculum is supporting data science students in identifying data biases and their consequences, critical attention to the cultural histories and vested interests animating data semantics is needed to elucidate the assumptions and political commitments on which data rest, along with the externalities they produce. In this article, I introduce three modes of reading that can be engaged when studying datasets—a denotative reading, a connotative reading, and (...) a deconstructive reading. I then outline how I have taught students to engage these methods when analyzing three datasets in Data and Society—a course designed to cultivate student competency in politically aware data analysis and interpretation. I show how combined, the reading strategies prompt students to grapple with the double binds of perceiving contemporary problems through systems of representation that are always situated, incomplete, and inflected with diverse politics. While I introduce these methods in the context of teaching, I argue that the methods are integral to any data practice in the conclusion. (shrink)

RESUMEN: Una crisis como la del coronavirus, que ha tenido lugar en un entorno digitalizado, ha puesto a prueba la capacidad y los límites del big data para proporcionarnos una imagen completa de la realidad e indicaciones para gestionar la pandemia. Uno de los aprendizajes que hemos de realizar es entender que los datos reflejan la desigualdad existente y, sin una correcta interpretación, invisibilizan a los grupos más vulnerables. Para ello lo más importante es dejar de concebir la realidad social (...) a partir de los criterios estandarizados y pensarla como compuesta de individuos y grupos. (shrink)