The applications of machine learning and deep learning to the natural sciences has fostered the idea that the automated nature of algorithmic analysis will gradually dispense human beings from scientific work. In this paper, I will show that this view is problematic, at least when ML is applied to biology. In particular, I will claim that ML is not independent of human beings and cannot form the basis of automated science. Computer scientists conceive their work as being (...) a case of Aristotle’s poiesis perfected by techne, which can be reduced to a number of straightforward rules and technical knowledge. I will show a number of concrete cases where at each level of computational analysis, more is required to ML than just poiesis and techne, and that the work of ML practitioners in biology needs also the cultivation of something analogous to phronesis, which cannot be automated. But even if we knew how to frame phronesis into rules, still this virtue is deeply entrenched in our biological constitution, which computers lack. Whether computers can fully perform scientific practice independently of humans is an ill-posed question. (shrink)

This book provides a critical reflection on automated science and addresses the question whether the computational tools we developed in last decades are changing the way we humans do science. More concretely: Can machines replace scientists in crucial aspects of scientific practice? The contributors to this book rethink and refine some of the main concepts by which science is understood, drawing a fascinating picture of the developments we expect over the next decades of human-machine co-evolution. The (...) volume covers examples from various fields and areas, such as molecular biology, climate modeling, clinical medicine, and artificial intelligence. The explosion of technological tools and drivers for scientific research calls for a renewed understanding of the human character of science. This book aims precisely to contribute to such a renewed understanding of science. (shrink)

The basis of science is the hypothetico-deductive method and the recording of experiments in sufficient detail to enable reproducibility. We report the development of Robot Scientist "Adam," which advances the automation of both. Adam has autonomously generated functional genomics hypotheses about the yeast Saccharomyces cerevisiae and experimentally tested these hypotheses by using laboratory automation. We have confirmed Adam's conclusions through manual experiments. To describe Adam's research, we have developed an ontology and logical language. The resulting formalization involves over 10,000 (...) different research units in a nested treelike structure, 10 levels deep, that relates the 6.6 million biomass measurements to their logical description. This formalization describes how a machine contributed to scientific knowledge. (shrink)

Philosophy Compass, Volume 17, Issue 1, January 2022.

The intention of this paper is to point to the dilemma humanity may face in light of AI advancements. The dilemma is whether to create a world with less evil or maintain the human status of moral agents. This dilemma may arise as a consequence of using automated decision-making systems for high-stakes decisions. The use of automated decision-making bears the risk of eliminating human moral agency and autonomy and reducing humans to mere moral patients. On the other hand, (...) it also has the potential to bring tremendous benefits to humanity by decreasing human-induced harm in the world. After presenting how this dilemma may arise, I explore general avenues for addressing it. I will argue that we do not have to solve this dilemma in an all-or-nothing fashion and that a more nuanced approach may be suitable. However, the main point I want to highlight is that we need to have a principled way of addressing this dilemma, which is currently missing. (shrink)

Artificial intelligence-based (AI) technologies such as machine learning (ML) systems are playing an increasingly relevant role in medicine and healthcare, bringing about novel ethical and epistemological issues that need to be timely addressed. Even though ethical questions connected to epistemic concerns have been at the center of the debate, it is going unnoticed how epistemic forms of injustice can be ML-induced, specifically in healthcare. I analyze the shortcomings of an ML system currently deployed in the USA to predict patients’ likelihood (...) of opioid addiction and misuse (PDMP algorithmic platforms). Drawing on this analysis, I aim to show that the wrong inflicted on epistemic agents involved in and affected by these systems’ decision-making processes can be captured through the lenses of Miranda Fricker’s account of hermeneutical injustice. I further argue that ML-induced hermeneutical injustice is particularly harmful due to what I define as an automated hermeneutical appropriation from the side of the ML system. The latter occurs if the ML system establishes meanings and shared hermeneutical resources without allowing for human oversight, impairing understanding and communication practices among stakeholders involved in medical decision-making. Furthermore and very much crucially, an automated hermeneutical appropriation can be recognized if physicians are strongly limited in their possibilities to safeguard patients from ML-induced hermeneutical injustice. Overall, my paper should expand the analysis of ethical issues raised by ML systems that are to be considered epistemic in nature, thus contributing to bridging the gap between these two dimensions in the ongoing debate. (shrink)

Robots are widely expected—and pushed—to transform open-field agriculture, but these visions remain wedded to optimizing monocultural farming systems. Meanwhile there is little pull for automation from ecology-based, diversified farming realms. Noting this gap, we here explore the potential for robots to foster an agroecological approach to crop production. The research was situated in The Netherlands within the case of _pixel cropping_, a nascent farming method in which multiple food and service crops are planted together in diverse assemblages employing agroecological practices (...) such as intercropping and biological pest control. Around this case we engaged with a variety of specialists in discussion groups, workshops, and design challenges to explore the potential of field robots to meet the multifaceted demands of highly diverse agroecological cropping systems. This generated a spectrum of imaginations for how automated tools might—or might not—be appropriately used, ranging from fully automated visions, to collaborative scenarios, to fully analogue prototypes. We found that automating agroecological cropping systems requires finding ways to imbue the ethos of agroecology into designed tools, thereby seeking to overcome tensions between production aims and other forms of social and ecological care. We conclude that a rethinking of automation is necessary for agroecological contexts: not as a blueprint for replacing humans, but making room for analogue and hybrid forms of agricultural work. These findings highlight a need for design processes which include a diversity of actors, involve iterative design cycles, and incorporate feedback between designers, practitioners, tools, and cropping systems. (shrink)

Advances in AI are powering increasingly precise and widespread computational propaganda, posing serious threats to national security. The military and intelligence communities are starting to discuss ways to engage in this space, but the path forward is still unclear. These developments raise pressing ethical questions, about which existing ethics frameworks are silent. Understanding these challenges through the lens of “cognitive security,” we argue, offers a promising approach.

Artificial intelligence is poised to eliminate millions of jobs, from finance to truck driving. But artisanal products are valued precisely because of their human origins, and thus have some inherent “immunity” from AI job loss. At the same time, artisanal labor, combined with technology, could potentially help to democratize the economy, allowing independent, small-scale businesses to flourish. Could AI, robotics and related automation technologies enhance the economic viability and environmental sustainability of these beloved crafting professions, perhaps even expanding their niche (...) to replace some job loss in other sectors? In this paper, we compare the problems created by the current mass production economy and potential solutions from an artisanal economy. In doing so, the paper details the possibilities of utilizing AI to support hybrid forms of human–machine production at the microscale; localized and sustainable value chains at the mesoscale; and networks of these localized and sustainable producers at the macroscale. In short, a wide range of automation technologies are potentially available for facilitating and empowering an artisanal economy. Ultimately, it is our hope that this paper will facilitate a discussion on a future vision for more “generative” economic forms in which labor value, ecological value and social value can circulate without extraction or alienation. (shrink)

Many ethicists writing about automated systems attribute agency to these systems. Not only that; they seemingly attribute an autonomous or independent form of agency to these machines. This leads some ethicists to worry about responsibility-gaps and retribution-gaps in cases where automated systems harm or kill human beings. In this paper, I consider what sorts of agency it makes sense to attribute to most current forms of automated systems, in particular automated cars and military robots. I argue (...) that whereas it indeed makes sense to attribute different forms of fairly sophisticated agency to these machines, we ought not to regard them as acting on their own, independently of any human beings. Rather, the right way to understand the agency exercised by these machines is in terms of human–robot collaborations, where the humans involved initiate, supervise, and manage the agency of their robotic collaborators. This means, I argue, that there is much less room for justified worries about responsibility-gaps and retribution-gaps than many ethicists think. (shrink)

Algorithmic decision making is used in an increasing number of fields. Letting automated processes take decisions raises the question of their accountability. In the field of computational journalism, the algorithmic accountability framework proposed by Diakopoulos formalizes this challenge by considering algorithms as objects of human creation, with the goal of revealing the intent embedded into their implementation. A consequence of this definition is that ensuring accountability essentially boils down to a transparency question: given the appropriate reverse-engineering tools, it should (...) be feasible to extract design criteria and to identify intentional biases. General limitations of this transparency ideal have been discussed by Ananny and Crawford :973–989, 2018). We further focus on its technical limitations. We show that even if reverse-engineering concludes that the criteria embedded into an algorithm correspond to its publicized intent, it may be that adversarial behaviors make the algorithm deviate from its expected operation. We illustrate this issue with an automated news recommendation system, and show how the classification algorithms used in such systems can be fooled with hard-to-notice modifications of the articles to classify. We therefore suggest that robustness against adversarial behaviors should be taken into account in the definition of algorithmic accountability, to better capture the risks inherent to algorithmic decision making. We finally discuss the various challenges that this new technical limitation raises for journalism practice. (shrink)

Certain characteristics make machine learning a powerful tool for processing large amounts of data, and also particularly unsuitable for explanatory purposes. There are worries that its increasing use in science may sideline the explanatory goals of research. We analyze the key characteristics of ML that might have implications for the future directions in scientific research: epistemic opacity and the ‘theory-agnostic’ modeling. These characteristics are further analyzed in a comparison of ML with the traditional statistical methods, in order to demonstrate (...) what it is specifically that makes ML methodology substantially unsuitable for reaching explanations. The analysis is given broader philosophical context by connecting it with the views on the role of prediction and explanation in science, their relationship, and the value of explanation. We proceed to show, first, that ML disrupts the relationship between prediction and explanation commonly understood as a functional relationship. Then we show that the value of explanation is not exhausted in purely predictive functions, but rather has a ubiquitously recognized value for both science and everyday life. We then invoke two hypothetical scenarios with different degrees of automatization of science, which help test our intuitions on the role of explanation in science. The main question we address is whether ML will reorient or otherwise impact our standard explanatory practice. We conclude with a prognosis that ML would diversify science into purely predictively oriented research based on ML-like techniques and, on the other hand, remaining faithful to anthropocentric research focused on the search for explanation. (shrink)

How should policymakers respond to the risk of technological unemployment that automation brings? First, I develop a procedure for answering this question that consults, rather than usurps, individuals’ own attitudes and ambitions towards that risk. I call this the insurance argument. A distinctive virtue of this view is that it dispenses with the need to appeal to a class of controversial reasons about the value of employment, and so is consistent with the demands of liberal political morality. Second, I appeal (...) to the insurance argument to show that governments ought not simply to provide those who are displaced by machines with unemployment benefits. Instead, it must offer re-training programmes, as well as enact more general macroeconomic policies that create new opportunities for employment. My contribution is important not only because it helps us to resolve a series of urgent policy disputes—disputes that have been discussed extensively by labour market economists and policymakers, but less so by political philosophers—but also because my analysis sheds light on more general philosophical controversies relating to risk. (shrink)

POLITICS is a system of computer programs which simulates humans in comprehending and responding to world events from a given political or ideological perspective. The primary theoretical motivations were: (1) the implemention of a functional system which applies the knowledge structures of Schank and Abelson (1977) to the domain of simulating political belief systems; (2) the development of a tentative theory of intentional goal conflicts and counterplanning. Secondary goals of the POLITICS project include developing a representation for belief systems, investigating (...) cognitive processes such as goal‐directed inferencing, and the integration of several types of knowledge representations into a functional system. (shrink)

Large language models and other generative artificial intelligence systems are achieving increasingly impressive results, though the quality of those results still seems dull and uninspired. This paper argues that this poor quality can be linked to the philosophical notion of inauthenticity as presented by Kierkegaard, Nietzsche, and Heidegger, and that this inauthenticity is fundamentally grounded in the design and structure of such systems by virtue of the way they statistically level down the materials on which they are trained. Although it (...) seems possible to create the conditions for authenticity in these systems, the resulting authenticity would be grounded in machine intelligence, not human intelligence. The argument extends the criticisms of artificial intelligence articulated by Hubert Dreyfus, updated to account for recent developments in machine learning and artificial neural networks. While more optimistic concerning the prospects for successfully creating artificial intelligence than Dreyfus had been, this paper argues that the resulting intelligence may not align well with human intelligence and may not be desirable for humans, if it is fully authentic. (shrink)

The paper explores some normative challenges concerning the integration of Machine Learning (ML) algorithms into anticorruption in public institutions. The challenges emerge from the tensions between an approach treating ML algorithms as allies to an exclusively legalistic conception of anticorruption and an approach seeing them within an institutional ethics of office accountability. We explore two main challenges. One concerns the variable opacity of some ML algorithms, which may affect public officeholders’ capacity to account for institutional processes relying upon ML techniques. (...) The other pinpoints the risk that automating certain institutional processes may weaken officeholders’ direct engagement to take forward-looking responsibility for the working of their institution. We discuss why both challenges matter to see how ML algorithms may enhance (and not hinder) institutional answerability practices. (shrink)

Automated Software Testing (AST) using Model Checking is in this article epistemologically analysed in order to argue in favour of a model-based reasoning paradigm in computer science. Preliminarily, it is shown how both deductive and inductive reasoning are insufficient to determine whether a given piece of software is correct with respect to specified behavioural properties. Models algorithmically checked in Model Checking to select executions to be observed in Software Testing are acknowledged as analogical models which establish isomorphic relations (...) with the target system’s data set. Analogical models developed in AST are presented as abductive models providing hypothetical explanations to observed executions. The model assumption—algorithmic check—software testing process is understood as the abduction—deduction—induction process defining the selective abduction and turned to isolate a set of model-based hypotheses concerning the target system behaviours. A manipulative abduction process is finally recognized in the practice of adapting, abstracting and refining models that do not provide successful predictions. (shrink)

This paper views the impact of automation and new globalization phenomenon, outsourcing on sustainable economic growth. Its main scope is to analyze the impacts of advanced automation and offshore outsourcing in manufacturing on a human capital and an acceleration of structural unemployment. Simultaneously, in the paper are compared two concepts by which a company can tend to attain a position of manufacturing excellence. Further is given a special emphasis to the automation driven shrinking of the middle class in countries that (...) are passing to the phase of deindustrialization. Finally some laws of automation in manufacturing are formulated and few ideas and recommendations for the future are outlined. (shrink)

nature of modern data collection and storage techniques, and the increases in the speed and storage capacities of computers. Statistics books from 30 years ago often presented examples with fewer than 10 variables, in domains where some background knowledge was plausible. In contrast, in new domains, such as climate research where satellite data now provide daily quantities of data unthinkable a few decades ago, fMRI brain imaging, and microarray measurements of gene expression, the number of variables can range into the (...) tens of thousands, and there is often limited background knowledge to reduce the space of alternative causal hypotheses. In such domains, non-automated causal discovery techniques appear to be hopeless, while the availability of faster computers with larger memories and disc space allow for the practical implementation of computationally intensive automated search algorithms over large search spaces. Contemporary science is not your grandfather’s science, or Karl Popper’s. Causal inference without experimental controls has long seemed as if it must somehow be capable of being cast as a kind of statistical inference involving estimators with some kind of convergence and accuracy properties under some kind of assumptions. Until recently, the statistical literature said not. While parameter estimation and experimental design for the effective use of data developed throughout the 20th century, as recently as 20 years ago the methodology of causal inference without experimental controls remained relatively primitive. Besides a cessation of hostilities from the majority of the statistical and philosophical communities (which has still only partially happened), several things were needed for theories of causal estimation to appear and to flower: well defined mathematical objects to represent causal relations; well defined connections between aspects of these objects and sample data; and a way to compute those connections. A sequence of studies beginning with Dempster’s work on the factorization of probability distributions [Dempster 1972] and culminating with Kiiveri and Speed’s [Kiiveri & Speed 1982] study of linear structural equation models, provided the first, in the form of directed acyclic graphs, and the second, in the form of the “local” Markov condition.. (shrink)

Important decisions that impact humans lives, livelihoods, and the natural environment are increasingly being automated. Delegating tasks to so-called automated decision-making systems can improve efficiency and enable new solutions. However, these benefits are coupled with ethical challenges. For example, ADMS may produce discriminatory outcomes, violate individual privacy, and undermine human self-determination. New governance mechanisms are thus needed that help organisations design and deploy ADMS in ways that are ethical, while enabling society to reap the full economic and social (...) benefits of automation. In this article, we consider the feasibility and efficacy of ethics-based auditing as a governance mechanism that allows organisations to validate claims made about their ADMS. Building on previous work, we define EBA as a structured process whereby an entity’s present or past behaviour is assessed for consistency with relevant principles or norms. We then offer three contributions to the existing literature. First, we provide a theoretical explanation of how EBA can contribute to good governance by promoting procedural regularity and transparency. Second, we propose seven criteria for how to design and implement EBA procedures successfully. Third, we identify and discuss the conceptual, technical, social, economic, organisational, and institutional constraints associated with EBA. We conclude that EBA should be considered an integral component of multifaced approaches to managing the ethical risks posed by ADMS. (shrink)

Human attention is limited in its capacity and duration. In language, this is manifested in many ways, but more conspicuously in the strategies by which information is distributed in utterances, that is, their information structures. We contend that the pragmatic categories of Topic and Presupposition precisely meet the necessity to modulate attentional resources on sentence contents, and they do this by “directing” certain contents to automatic and others to controlled processing mechanisms. We discuss experimental findings suggesting that presupposed or topicalized (...) information correlates with automatic processing, and we suggest that this association grounded for the emergence of topic and presupposition units in human communication. We also put forth the processing automaticity induced by these units as the rationale behind their persuasiveness in some specific contexts of language use. (shrink)

Organisations increasingly use automated decision-making systems (ADMS) to inform decisions that affect humans and their environment. While the use of ADMS can improve the accuracy and efficiency of decision-making processes, it is also coupled with ethical challenges. Unfortunately, the governance mechanisms currently used to oversee human decision-making often fail when applied to ADMS. In previous work, we proposed that ethics-based auditing (EBA)—that is, a structured process by which ADMS are assessed for consistency with relevant principles or norms—can (a) help (...) organisations verify claims about their ADMS and (b) provide decision-subjects with justifications for the outputs produced by ADMS. In this article, we outline the conditions under which EBA procedures can be feasible and effective in practice. First, we argue that EBA is best understood as a ‘soft’ yet ‘formal’ governance mechanism. This implies that the main responsibility of auditors should be to spark ethical deliberation at key intervention points throughout the software development process and ensure that there is sufficient documentation to respond to potential inquiries. Second, we frame AMDS as parts of larger sociotechnical systems to demonstrate that to be feasible and effective, EBA procedures must link to intervention points that span all levels of organisational governance and all phases of the software lifecycle. The main function of EBA should, therefore, be to inform, formalise, assess, and interlink existing governance structures. Finally, we discuss the policy implications of our findings. To support the emergence of feasible and effective EBA procedures, policymakers and regulators could provide standardised reporting formats, facilitate knowledge exchange, provide guidance on how to resolve normative tensions, and create an independent body to oversee EBA of ADMS. (shrink)

A Quantum-Mechanical automation, equipped with mechanisms for the measurement and the recording and the prediction of certain physical properties of the world, is described. It is inquired what sort of empirical description such an automation would produce of itself. It turns out that this description would be a very novel one, one such as was never imagined in the conventional discussions of measurement.

Fueled by ever-growing amounts of (digital) data and advances in artificial intelligence, decision-making in contemporary societies is increasingly delegated to automated processes. Drawing from social science theories and from the emerging body of research about algorithmic appreciation and algorithmic perceptions, the current study explores the extent to which personal characteristics can be linked to perceptions of automated decision-making by AI, and the boundary conditions of these perceptions, namely the extent to which such perceptions differ across media, (public) (...) health, and judicial contexts. Data from a scenario-based survey experiment with a national sample (N = 958) show that people are by and large concerned about risks and have mixed opinions about fairness and usefulness of automated decision-making at a societal level, with general attitudes influenced by individual characteristics. Interestingly, decisions taken automatically by AI were often evaluated on par or even better than human experts for specific decisions. Theoretical and societal implications about these findings are discussed. (shrink)

This thesis examines the prospects for mechanical procedures that can identify true, complete, universal, first-order logical theories on the basis of a complete enumeration of true atomic sentences. A sense of identification is defined that is more general than those which are usually studied in the learning theoretic and inductive inference literature. Some identification algorithms based on confirmation relations familiar in the philosophy of science are presented. Each of these algorithms is shown to identify all purely universal theories without (...) function symbols. It is demonstrated that no procedure can solve this universal theory inference problem in the more usual senses of identification. The question of efficiency for theory inference systems is addressed, and some definitions of limiting complexity are examined. It is shown that several aspects of obvious strategies for solving the universal theory inference problem are NP-hard. Finally, some non-worst case heuristic search strategies are examined in light of these NP-completeness results. These strategies are based upon an isomorphism between clausal entailments of a certain class and partition lattices, and are applicable to the improvement of earlier work on language acquisition and logical inductive inference. (shrink)

In this paper, I will argue that automated vehicles should not swerve to avoid a person or vehicle in its path, unless they can do so without imposing risks onto others. I will argue that this is the conclusion that we should reach even if we start by assuming that we should divert the trolley in the standard trolley case. In defence of this claim, I appeal to the distribution of moral and legal responsibilities, highlighting the importance of safe (...) spaces, and arguing in favour of constraints on what can be done to minimise casualties. My arguments draw on the methodology associated with the trolley problem. As such, this paper also defends this methodology, highlighting a number of ways in which authors misunderstand and misrepresent the trolley problem. For example, the ‘trolley problem’ is not the ‘name given by philosophers to classic examples of unavoidable crash scenarios, historically involving runaway trolleys’, as Millar suggests, and trolley cases should not be compared with ‘model building in the sciences’, as Gogoll and Müller suggest. Trolley cases have more in common with lab experiments than model building, and the problem referred to in the trolley problem is not the problem of deciding what to do in any one case. Rather, it refers to the problem of explaining what appear to be conflicting intuitions when we consider two cases together. The problem, for example, could be: how do we justify the claim that automated vehicles should not swerve even if we accept the claim that we should divert the trolley in an apparently similar trolley case? (shrink)

This study examines the consequences of computerization for women who do information work. Syntheses of research findings from both the general social science literature and the business and management periodical literature are compared with each other. The two bodies of research results converge with respect to employment consequences and shifts in work, but differ markedly when it comes to control of the labor process and training. In contrast to social scientists, management researchers pay scant attention to differential gender effects (...) of microcomputer deployment. Similarities and differences between the two research traditions show that social science and business research, if combined, yield a better understanding of the changes prompted by new information technology. (shrink)

We claim the climate crisis is the proper baseline for establishing the terms of debate about the viability of a post-work future. In this paper, we aim to assess the viability of a post-work future in which automation replaces a significant portion of human labor. We do this by laying out the possible outcomes of what such a future will look like based on three related axes: technological capacity, politics and social distribution, and alternative conceptions of the good. The purpose (...) in tracking these outcomes is to have a clear picture of those outcomes we should be pursuing in contrast to those we ought to avoid. Put another way, to achieve a post-work future that is egalitarian in character will require ensuring that technological capacity is used to improve the prospects for all humanity. The argument proceeds along the following lines. First, we summarize the scientific consensus about the causes and consequences of climate change, as well as the tools of mitigation and adaptation it recommends. The causes are primarily rooted in the global system of production and consumption that has developed over the last century. We refer to this pattern of development as the “Growth Model” since it primarily relies on energy created from burning fossil fuels and the exponential consumption of resources to promote more growth (Rose 2020). Second, we construct a range of possible outcomes regarding the climate crisis that are based on different trajectories of development. We do so by examining each of two cases located on the poles of three different axes. These axes include technology, political organization and social distribution, and conceptions of the good, yielding eight possible outcomes. The point of considering these cases and how they intersect along these axes is two-fold: to clarify the features that define them and compare their merits and drawbacks, enabling us to formulate the means for avoiding some outcomes and strive for others. Third, we then consider the viability of a post-work future in terms of these outcomes. Some of these outcomes point in a direction where automation replaces labor, and its benefits are shared widely. Others indicate a future in which some groups benefit at the expense of others, while some appear to preclude the emergence of such a future altogether. Finally, we conclude with some observations about the viability of a post-work future given our evaluation of these outcomes. (shrink)

Karl Marx took technological development to be the heart of capitalism’s drive and, ultimately, its undoing. Machines are initially engineered to perform functions that otherwise would be performed by human workers. The economic logic pushed to its limits leads to the prospect of full automation: a world in which all labor required to meet human needs is superseded and performed by machines. To explore the future of automation, the paper considers a specific point of resemblance between human beings and machines: (...) intelligence. Examining the development of machine intelligence through the Marxist concepts of alienation and reification reveals a tension between certain technophilic post-labor visions and the reality of capitalistic development oriented towards intelligent technology. If the prospect of a post-labor world depends on technologies that closely resemble humans, the world can no longer be described as post-labor. The tension has implications for the potential moral status of machines and the possibility of full automation. The paper considers these implications by outlining four possible futures of automation. (shrink)

Written as the introduction to a special issue of Body & Society on the topic of animation and automation, this article considers the interrelation of those two terms through readings of relevant work in film studies and science and technology studies (STS), inflected through recent scholarship on the body. Drawing upon historical and contemporary examples, we trace how movement is taken as a sign of life, while living bodies are translated through the mechanisms of artifice. Whereas film studies has (...) drawn upon work ranging from production history to semiotics and psychoanalysis to conceptualize the ways in which the appearance of life on the cinema screen materializes subjectivities beyond it, STS has developed a corpus of theoretical and empirical scholarship that works to refigure material-semiotic entanglements of subjects and objects. In approaching animation and automation through insights developed within these two fields we hope to bring them into closer dialogue with each other and with studies of the body, given the convergence of their shared concerns with affective materializations of life. More specifically, an interest in the moving capacities of animation, and in what gets rendered invisible in discourses of automation, is central to debates regarding the interdependencies of bodies and machines. Animation is always in the end a relational effect, it seems, while automation implies the continuing presence of hidden labour and care. (shrink)

The aim of this essay is to examine the long-term evolution of the material reproductive vehicles of society. The fairly continuous trend of economic integration and progressive enfranchisement of the world’s people is indicated, ascertainable even with the emergence from general slavery of ancient times, through feudalism to the modern stage of industrialism and widespread national sovereignties. With greater political expression has come higher degrees and penetration of economic prosperity. Both vicious and virtuous tendencies of automation are considered. The necessary (...) foundation of living labor is recognized. (shrink)

This paper discusses how computational modeling combines the autonomy of models with the automation of computational procedures. In particular, the case of ab-initio methods in quantum chemistry will be investigated to draw two lessons from the analysis of computational modeling. The first belongs to general philosophy of science: Computational modeling faces a trade-off and enlarges predictive force at the cost of explanatory force. The other lesson is about the philosophy of chemistry: The methodology of computational modeling puts into doubt (...) claims about the reduction of chemistry to physics. (shrink)

Automated technologies and robots make decisions that cannot always be fully controlled or predicted. In addition to that, they cannot respond to punishment and blame in the ways humans do. Therefore, when automated cars harm or kill people, for example, this gives rise to concerns about responsibility-gaps and retribution-gaps. According to Sven Nyholm, however, automated cars do not pose a challenge on human responsibility, as long as humans can control them and update them. He argues that the (...) agency exercised in automated cars should be understood in terms of human–robot collaborations. This brief note focuses on the problem that arises when there are multiple people involved, but there is no obvious shared collaboration among them. Building on John Danaher’s discussion of command responsibility, it is argued that, although Nyholm might be right that autonomous cars cannot be regarded as acting on their own, independently of any human beings, worries about responsibility-gaps and retribution-gaps are still justified, because it often remains unclear how to allocate or distribute responsibility satisfactorily among the key humans involved after they have been successfully identified. (shrink)

Amazon’s projects for future automation contribute to anxieties about the marginalization of living labor in warehousing. Yet, a systematic analysis of patents owned by Amazon suggests that workers are not about to disappear from the warehouse floor. Many patents portray machines that increase worker surveillance and work rhythms. Others aim at incorporating workers’ activities into machinery to rationalize the labor process in an ever more pervasive form of digital Taylorism. Patents materialize the company’s desire for a technological future in which (...) workers act and sense on behalf of machinery, becoming its living and sensing appendages. In this new relationship, humans extend machinery and its reach. Through the work-in-progress process of reaching increasing levels of automation, Amazon develops new technical foundations that consolidate its power in the digital workplace. (shrink)

La classification de Wolfram des automates cellulaires déterministes repose sur l’analogie entre le comportement dynamique des automates et celui de systèmes physiques au cours d’une transition de phase. Pour évaluer la valeur scientifique de la classification, longuement débattue, on doit s’interroger sur les caractéristiques de cette analogie. Nous établissons ici quels éléments, présents dans les transitions de phase, n’ont pas d’équivalent dans le domaine des automates. Ensuite, nous discutons la notion de potentiel d’une analogie en la comparant à deux autres (...) exemples de la littérature. (shrink)

The development of computer science has raised ethical concerns regarding the potential negative impacts of machine learning tools on people and society. Some examples are pornographic deepfakes used as weapons of war against women; pattern recognition designed to uncover sexual orientation; and misuse of data and deep learning by private companies to influence democratic elections. We contend that these three examples are cases of automated evil. In this article, we defend that the concept of forbidden knowledge can help (...) to inform a coherent ethical framework in the context of machine learning research. We conclude that restricting generalised access to extensive data and limiting access to ready-to-use codes would mitigate potential harms caused by machine learning tools. In addition, the notions of intersectionality and interdisciplinarity should be systematically introduced in data and computer science research. (shrink)

All of finance is now automated, most notably high frequency trading. This paper examines the ethical implications of this fact. As automation is an interdisciplinary endeavor, we argue that the interfaces between the respective disciplines can lead to conflicting ethical perspectives; we also argue that existing disciplinary standards do not pay enough attention to the ethical problems automation generates. Conflicting perspectives undermine the protection those who rely on trading should have. Ethics in finance can be expanded to include organizational (...) and industry-wide responsibilities to external market participants and society. As a starting point, quality management techniques can provide a foundation for a new cross-disciplinary ethical standard in the age of automation. (shrink)

All of finance is now automated, most notably high frequency trading. This paper examines the ethical implications of this fact. As automation is an interdisciplinary endeavor, we argue that the interfaces between the respective disciplines can lead to conflicting ethical perspectives; we also argue that existing disciplinary standards do not pay enough attention to the ethical problems automation generates. Conflicting perspectives undermine the protection those who rely on trading should have. Ethics in finance can be expanded to include organizational (...) and industry-wide responsibilities to external market participants and society. As a starting point, quality management techniques can provide a foundation for a new cross-disciplinary ethical standard in the age of automation. (shrink)

The role of automated or semiautomated social media accounts, commonly known as “bots,” in social and political processes has gained significant scholarly attention. The current body of research discusses how bots can be designed to achieve specific purposes as well as instances of unexpected negative outcomes of such use. We suggest that the interplay between social media affordances and user practices can result in incidental effects from automated agents. We examined a Twitter network data set with 1,782 nodes (...) and 5,640 edges to demonstrate the engagement and outreach of a retweeting bot called Siripalabot that was popular among Sri Lankan Twitter users. The bot served the simple function of retweeting tweets with hashtags #SriLanka and #lk to its follower network. However, the co-use of #Sri Lanka and/or #lk with #PresPollSL, a hashtag used to discuss politics related to Sri Lanka’s presidential election in 2015, resulted in the bot incidentally amplifying the political voice of less engaged actors. The analysis demonstrated that the bot dominated the network in terms of engagement (out-degree) and the ability to connect distant clusters of actors (betweenness centrality) while more traditional actors, such as the main election candidates and news accounts, indicated more prestige (in-degree) and power (eigenvector centrality). We suggest that the study of automated agents should include designer intentions, the design and behavior of automated agents, user expectations, as well as unintended and incidental effects of interaction. (shrink)

With slogans such as ‘Tell the stories hidden in your data’ and ‘From data to clear, insightful content – Wordsmith automatically generates narratives on a massive scale that sound like a person crafted each one’, a series of companies currently market themselves on the ability to turn data into stories through Natural Language Generation techniques. The data interpretation and knowledge production process is here automated, while at the same time hailing narrativity as a fundamental human ability of meaning-making. Reading (...) both the marketing rhetoric and the functionality of the automated narrative services through narrative theory allows for a contextualization of the rhetoric flourishing in Big Data discourse. Building upon case material obtained from companies such as Arria NLG, Automated Insights, Narrativa, Narrative Science, and Yseop, this article argues that what might be seen as a ‘re-turn’ of narrative as a form of knowledge production that can make sense of large data sets inscribes itself in – but also rearticulates – an ongoing debate about what narrative entails. Methodological considerations are thus raised on the one hand about the insights to be gained for critical data studies by turning to literary theory, and on the other hand about how automated technologies may inform our understanding of narrative as a faculty of human meaning-making. (shrink)

An emerging discourse about automated agricultural machinery imagines farms as places where farmers and workers do not need to be, but also implicitly frames farms as intolerable places where people do not want to be. Only autonomous machines, this story goes, can relieve farmers and workers of this presumed burden by letting them ‘farm at a distance’. In return for this distanced autonomy, farmers are promised increased control over their work-life balance and greater farm productivity from letting ‘smart’ robots (...) assume control over the operational environment. Drawing upon the ways that these machines are promoted by manufacturers in various media, we trace the nascent contours of what we term a liberatory sociotechnical imaginary for agricultural automation across three cases—automated milking systems, self-driving tractors, and robotic strawberry pickers. We show how promises for new freedoms and autonomy are flexibly deployed to respond to distinct frictions that farmers, workers, and even farm animals experience in different modes of industrial agriculture. However, underlying these promises is the purposefully understated self-interest of manufacturers, who stand to gain further control over farms if automated technologies assume a central role in agriculture. Through the liberatory rhetoric, we contend, the imaginary seeks to enroll farmers into a socio-technical network that creates new relations of dependence upon the companies who design, sell, maintain, and often retain ownership over automated technologies. While potentially powerful, this imaginary may nonetheless fail to coalesce as farmers, workers, and agroecosystems exert their own agency on automated imaginaries and technological futures for agriculture. (shrink)

Automation is once again raising concerns about the threat it poses to employment. Feminists in the 20th century believed that technology could liberate women from undesirable labor. However, historically, industry and automation have not reduced women’s workloads but have instead favored unpaid work, flexibility, and work overload. Rather than mitigating the care and ecological crises, technological development has exacerbated them. This raises an important question for feminist theory: should technology be rejected as a way of reducing women’s workload? To explore (...) this, we analyze classical and contemporary contributions from feminist theorists on the future of work and technology. Using philosophical and feminist theoretical methods, our aim is to examine the relationships between these imaginings and home, family, design, and consumption. This article argues that a feminist analysis of work must include the technological dimension, considering the production of human beings as a strategic technology for feminist purposes. To imagine an alternative near future, the article draws on Donna Haraway’s making kin, emphasizing defamiliarization and refamiliarization of social and ecological relations, pluralist science, and technology for sustainable regeneration of life; and finally, as suggested in this analysis, the ironic persistence of labour in a post-industrial or post-capitalist era. (shrink)

We are currently witnessing a sharp rise in the use of algorithmic decision-making tools. In these instances, a new wave of policy concerns is set forth. This article strives to map out these issues, separating the wheat from the chaff. It aims to provide policy makers and scholars with a comprehensive framework for approaching these thorny issues in their various capacities. To achieve this objective, this article focuses its attention on a general analytical framework, which will be applied to a (...) specific subset of the overall discussion. The analytical framework will reduce the discussion to two dimensions, every one of which addressing two central elements. These four factors call for a distinct discussion, which is at times absent in the existing literature. The two dimensions are the specific and novel problems the process assumedly generates and the specific attributes which exacerbate them. While the problems are articulated in a variety of ways, they most likely could be reduced to two broad categories: efficiency and fairness-based concerns. In the context of this discussion, such problems are usually linked to two salient attributes the algorithmic processes feature—its opaque and automated nature. (shrink)

The growing use of automated decision-making (ADM) systems in the public sector and the need to control these has raised many legal questions in academic research and in policymaking. One of the timely means of legal control is accountability, which traditionally includes the ability to impose sanctions on the violator as one dimension. Even though many risks regarding the use of ADM have been noted and there is a common will to promote the safety of these systems, the relevance (...) of the safety research has been discussed little in this context. In this article, I evaluate regulating accountability over the use of ADM in the public sector in relation to the findings of safety research. I conducted the study by focusing on ongoing regulatory projects regarding ADM, the Finnish ADM legislation draft and the EU proposal for the AI Act. The critical question raised in the article is what the role of sanctions is. I ask if official accountability could mean more of an opportunity to learn from mistakes, share knowledge and compensate for harm instead of control via sanctions. (shrink)