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utilitarian techno-economic categories, that is, oriented to assess (a) the technical feasibility of automation and (b) the cost and, therefore, any competitive advantage that could derive from implementation. Less attention was paid to other important non-techno-economic factors – sociotechnical, cognitive-behavioural, historical and cultural – that constitute the core of the interdisciplinary approach of STS. In this context, the application of digital technologies and artificial intelligence to medical imaging constitutes an ideal case for this dominant cognitive

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systems? Taking a step back, the question we will consider is how these processes of digging become algorithmic and what this will mean. We also seek to understand the very processes behind the automation of the excavation and targeting of memory. Walter Benjamin’s comments give us only a starting point. We use them here as an impression of how memory might be understood. Much has changed since he wrote that fragment. We do not wish to capture all of those changes to memory making in this book, although these varied technological shifts clearly include the emergence

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The elementary task of the administration, taking care of people’s basic and human rights, will be significantly enhanced when it is possible to meet people’s personal needs without committing to time and place, mechanically. MoE, 2017: 34 (Era of AI) In line with the goal of Finnish policies on AI to harness the potential of algorithms in business and everyday life ( MoE, 2019 ), several Finnish public authorities have, in recent years, adopted automation to enhance the quality of public services ( Chiusi et al, 2020 ). A key objective of these

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Introduction Automation is a persistent feature of the capitalist mode of production, yet societies have only reflected on it in recurring waves ( Benanav, 2020 ). The current wave focuses on how advanced robotics and machine learning will overhaul the labour market and might lead to mass unemployment and a devaluation of job qualifications. The scholarly proponents of the automation discourse are labour economists of the routine-biased technological change (RBTC) approach, arguing that manual and cognitive routine tasks are prone to be technically replaced

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James Wright (2023) Robots Won’t Save Japan: An Ethnography of Eldercare Automation ILR Press 182 pp Hardcover: ISBN 9781501768057, $49.95   I was reading Robots Won’t Save Japan on a train when another passenger complimented me on my reading choice – seemingly just because it was about Japan. He extolled the ‘delicacy’ of Japanese incense and perfumes, then noted that this virtue was offset by an ‘alienation and isolation’. This interaction served as a dully predictable reminder of ‘othering’, which extends from this person’s view of Japanese

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‘algorithmic management’. This term goes back to the work of Aneesh (2009) who describes it as a form of labour control that functions ‘by shaping an environment […] in which there are only programmed alternatives for the execution’ of the work tasks (p 71). As research discussions during the recent years have shown, however, the term ‘algorithmic management’ is often used in a variety of different ways. One group of researchers has highlighted the automation of task assignment and surveillance. Möhlmann and Zalmanson (2017) provided one of the first definitions of

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Does paid work as we know it meet basic standards of social justice? This is the main question that drives this book. At stake are both levels of pay and our emotional and physical wellbeing. The owners of capital still make the lion’s share of the decisions regarding the technology, design of jobs and the command systems that govern our workplaces. But as history shows, governments also play a major role in deciding when the prerogatives of employers trump their duty of care to employees. As the challenges of automation and globalisation reshape the nature

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In recent years, the United Kingdom's Home Office has started using automated systems to make immigration decisions. These systems promise faster, more accurate, and cheaper decision-making, but in practice they have exposed people to distress, disruption, and even deportation.

This book identifies a pattern of risky experimentation with automated systems in the Home Office. It analyses three recent case studies including: a voice recognition system used to detect fraud in English-language testing; an algorithm for identifying ‘risky’ visa applications; and automated decision-making in the EU Settlement Scheme.

The book argues that a precautionary approach is essential to ensure that society benefits from government automation without exposing individuals to unacceptable risks.

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The three systems we have explored in this book barely scratch the surface of automation in government immigration systems. They are systems which have, for various reasons and through various means, come into public view. But automated systems are being developed and deployed in many more corners of the immigration bureaucracy. The current trajectory, both in the UK and around the world, is toward increasingly automated immigration systems. From the transitional and experimental phase that we are currently in, it is clear that automated immigration systems

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, functions and physical structures, was shifting by the day. Automation had long been a preoccupation of stock exchange officials. Often, it had egalitarian underpinnings: if automation could reduce manpower, wrote one author, ‘we might even reduce the costs to such an extent that small orders became profitable and the ideal of the Cloth Cap Investor at last became a reality.’ 7 Fischer Black, the economist whose option pricing theory was then transforming the financial world, had dreamed of a fully automated securities market. His pamphlet on the topic was illustrated

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