There has been considerable debate around the ethical issues raised by data-driven technologies such as artificial intelligence. Ethical principles for the field have focused on the need to ensure...

Materialization practices in the architecture and building industry have evolved with the advancement of manufacturing and information technologies. This evolution is evident across various design and production phases, with a pronounced impact on prototyping. Advances in design and fabrication tools have empowered prototypes, integral in any production cycle, to furnish a growing array of information and feedback for designers and manufacturers. In this context, prototypes have transformed from merely showcasing data-driven building solutions to presenting socio-environmentally conscious systems. (...) class='Hi'>Innovation in prototyping connects the initial design and construction stages to the operational phase, creating a seamless transition throughout the project lifecycle. This chapter provides a range of definitions and prototypical case studies for smart prototyping by identifying practiced approaches in integrated design to production workflows. This chapter introduces three paradigms for smart prototyping: Digital prototyping focuses on data-driven design for mass customization, phygital prototyping involves mixed-reality-enabled design and assembly, and thirdly collaborative prototyping explores human-machine hybrid intelligence and co-production in architectural and urban contexts. The chosen case studies in this chapter and how they are categorized aim to provide a comprehensive overview of smart prototyping, covering projects conducted in both research and practice. This chapter concludes with potential future trends and the role of emerging and evolving mediums of prototyping for smart design and construction. (shrink)

Medical-legal partnerships connect legal advocates to healthcare providers and settings. Maintaining effectiveness of medical-legal partnerships and consistently identifying opportunities for innovation and adaptation takes intentionality and effort. In this paper, we discuss ways in which our use of data and quality improvement methods have facilitated advocacy at both patient (client) and population levels as we collectively pursue better, more equitable outcomes.

In this paper, we use an innovative, empirical, and–as yet–rarely applied method in bioethics, namely corpus analysis, which is commonly used in literature studies (Moretti 2013), linguistics (Bake...

Purpose:On the background of innovation-driven growth strategy of the Chinese government, this study aims to explore the impact of the knowledge base on innovation-driven growth of a firm, which is moderated by organizational character.Design/methodology/approach:Based on the data of 965 Chinese listed companies, some hypotheses were tested using the method of hierarchical regression analysis.Findings:Organizational growth relies on both technological and business model innovations and their interactive effect. Knowledge base, both breadth and depth, makes a positive impact (...) on the innovation-driven growth of an enterprise. In the impacting mechanism, an explicit organizational character not only has direct positive effects on business model innovation, it also strengthens the effect of knowledge breadth on business model innovation. On the contrary, an implicit organizational character is not significantly related to innovation.Research limitations/implications:In order to achieve growth, enterprises are suggested to adopt such dual innovation strategy, led by technological innovation and supplemented with business model innovation, which is supported by the integrated management of intangible resources, deep and broad knowledge, and explicit organizational character.Originality/value:A new theoretical framework of organizational innovation-driven growth was proposed. The realization paths of innovation-driven growth were explored. The idea of collaborative governance between the knowledge base and organizational character was raised. (shrink)

It is widely acknowledged that trust plays an important role for the acceptability of data sharing practices in research and healthcare, and for the adoption of new health technologies such as AI. Yet there is reported distrust in this domain. Although in the UK, the NHS is one of the most trusted public institutions, public trust does not appear to accompany its data sharing practices for research and innovation, specifically with the private sector, that have been introduced (...) in recent years. In this paper, we examine the question of, what is it about sharing NHS data for research and innovation with for-profit companies that challenges public trust? To address this question, we draw from political theory to provide an account of public trust that helps better understand the relationship between the public and the NHS within a democratic context, as well as, the kind of obligations and expectations that govern this relationship. Then we examine whether the way in which the NHS is managing patient data and its collaboration with the private sector fit under this trust-based relationship. We argue that the datafication of healthcare and the broader ‘health and wealth’ agenda adopted by consecutive UK governments represent a major shift in the institutional character of the NHS, which brings into question the meaning of public good the NHS is expected to provide, challenging public trust. We conclude by suggesting that to address the problem of public trust, a theoretical and empirical examination of the benefits but also the costs associated with this shift needs to take place, as well as an open conversation at public level to determine what values should be promoted by a public institution like the NHS. (shrink)

BackgroundReducing the number of animals used in experiments has become a priority for the governments of many countries. For these reductions to occur, animal-free alternatives must be made more available and, crucially, must be embraced by researchers.MethodsWe conducted an international online survey for academics in the field of animal science (N = 367) to explore researchers’ attitudes towards the implementation of animal-free innovations. Through this survey, we address three key questions. The first question is whether scientists who use animals in (...) their research consider governmental goals for animal-free innovations achievable and whether they would support such goals. Secondly, responders were asked to rank the importance of ten roadblocks that could hamper the implementation of animal-free innovations. Finally, responders were asked whether they would migrate (either themselves or their research) if increased animal research regulations in their country of residence restricted their research.ResultsWhile nearly half (40%) of the responders support governmental goals, the majority (71%) of researchers did not consider such goals achievable in their field within the near future. In terms of roadblocks for implementation of animal-free methods, ~ 80% of the responders considered ‘reliability’ as important, making it the most highly ranked roadblock. However, all other roadblocks were reported by most responders as somewhat important, suggesting that they must also be considered when addressing animal-free innovations. Importantly, a majority reported that they would consider migration to another country in response to a restrictive animal research policy. Thus, governments must consider the risk of researchers migrating to other institutes, states or countries, leading to a ‘brain-drain’ if policies are too strict or suitable animal-free alternatives are not available.ConclusionOur findings suggest that development and implementation of animal-free innovations are hampered by multiple factors. We outline three pillars concerning education, governmental influence and data sharing, the implementation of which may help to overcome these roadblocks to animal-free innovations. (shrink)

Engaging citizens with digital technology to co-create data, information and knowledge has widely become an important strategy for informing the policy response to COVID-19 and the ‘infodemic’ of misinformation in cyberspace. This move towards digital citizen participation aligns well with the United Nations’ agenda to encourage the use of digital tools to enable data-driven, direct democracy. From data capture to information generation, and knowledge co-creation, every stage of the data lifecycle bears important considerations to inform (...) policy and practice. Drawing on evidence of participatory policy and practice during COVID-19, we outline a framework for citizen ‘e-participation’ in knowledge co-creation across every stage of the policy cycle. We explore how coupling the generation of information with that of social capital can provide opportunities to collectively build trust in institutions, accelerate recovery and facilitate the ‘e-society’. We outline the key aspects of realising this vision of data-driven direct democracy by discussing several examples. Sustaining participatory knowledge co-creation beyond COVID-19 requires that local organisations and institutions incorporate adaptive learning mechanisms into their operational and governance structures, their integrated service models, as well as employing emerging social innovations. (shrink)

This ground-breaking and timely book explores how big data, artificial intelligence and algorithms are creating new types of agency, and the impact that this is having on our lives and the rule of law. Addressing the issues in a thoughtful, cross-disciplinary manner, the authors examine the ways in which data-driven agency is transforming democratic practices and the meaning of individual choice. Leading scholars in law, philosophy, computer science and politics analyse the latest innovations in data science (...) and machine learning, assessing the actual and potential implications of these technologies. They investigate how this affects our understanding of such concepts as agency, epistemology, justice, transparency and democracy, and advocate a precautionary approach that takes the effects of data-driven agency seriously without taking it for granted. Scholars and students of law, ethics and philosophy, in particular legal, political and democratic theory, will find this book a compelling and invaluable read, as will computer scientists interested in the implications of their own work. It will also prove insightful for academics and activists working on privacy, fairness and anti-discrimination. Contributors include: J.E. Cohen, G. de Vries, S. Delacroix, P. Dumouchel, C. Ess, M. Garnett, E.H. Gerding, R. Gomer, C. Graber, M. Hildebrandt, C. Maple, K. O'Hara, P. Ohm, m.c. schraefel, D. Stevens, N. van Dijk, M. Veale. (shrink)

This article offers several contributions to the interdisciplinary project of responsible research and innovation in data science and AI. First, it provides a critical analysis of current efforts to establish practical mechanisms for algorithmic auditing and assessment to identify limitations and gaps with these approaches. Second, it provides a brief introduction to the methodology of argument-based assurance and explores how it is currently being applied in the development of safety cases for autonomous and intelligent systems. Third, it generalises (...) this method to incorporate wider ethical, social, and legal considerations, in turn establishing a novel version of argument-based assurance that we call ‘ethical assurance.’ Ethical assurance is presented as a structured method for unifying the myriad practical mechanisms that have been proposed. It is built on a process-based form of project governance that enlists reflective innovation practices to operationalise normative principles, such as sustainability, accountability, transparency, fairness, and explainability. As a set of interlocutory governance mechanisms that span across the data science and AI lifecycle, ethical assurance supports inclusive and participatory ethical deliberation while also remaining grounded in social and technical realities. Finally, this article sets an agenda for ethical assurance, by detailing current challenges, open questions, and next steps, which serve as a springboard to build an active (and interdisciplinary) research programme as well as contribute to ongoing discussions in policy and governance. (shrink)

Through hypothetical scenarios, this paper analyses whether machine learning (ML) could resolve one of the main shortcomings present in Christopher Boorse’s Biostatistical Theory of health (BST). In doing so, it foregrounds the boundaries and challenges of employing ML in formulating a naturalist (i.e., prima facie value-free) definition of health. The paper argues that a sweeping dataist approach cannot fully make the BST truly naturalistic, as prior theories and values persist. It also points out that supervised learning introduces circularity, rendering it (...) incompatible with a naturalistic perspective. Additionally, it underscores the need for pre-existing auxiliary theories to assess results from unsupervised learning. It emphasizes the importance of understanding the epistemological entanglements between data and data processing methods to manage expectations about what data patterns can predict. In conclusion, the paper argues against delegating the final authority for defining complex concepts like health to AI systems, as it necessitates ethical judgment and capacities for deliberation that AI currently lacks. It also warns against granting creators and deployers of AI systems the discretionary authority to determine these definitions outside the wider social discussion, advocating for ongoing public engagement on normative notions. Failure to do so risks limiting individuals and collectives’ ability to shape a just digital future and diminishes their fundamental epistemic agency. (shrink)

Recent developments in global business gave rise to innovative forms of digital-exchange, facilitated by a new big data-based infrastructure – the sharing economy platform. SEPs are rapidly expanding, challenging the political-economic order and the classical division of work in society. Against the background of the current sustainability crisis, we discuss the increasingly momentous role of SEPs as a potential driver toward a more sustainable economy and society. Drawing on the theoretical lens of political CSR theory, we first outline how (...) SEPs are applying institutional strategies to gain a competitive advantage, thereby shaping contexts in their favor and triggering an erosion of foundations of social sustainability. However, SEPs also strive for legitimacy by taking over novel social responsibilities via profound digital capacities. Thus SEPs carry the potential to contribute to a more sustainable future economy. Our chapter considers future pathways of platform CSR regimes and a democratization of SEPs building on political CSR theory. (shrink)

BackgroundThe public and healthcare workers have a high expectation of animal research which they perceive as necessary to predict the safety and efficacy of drugs before testing in clinical trials. However, the expectation is not always realised and there is evidence that the research often fails to stand up to scientific scrutiny and its 'predictive value' is either weak or absent.DiscussionProblems with the use of animals as models of humans arise from a variety of biases and systemic failures including: 1) (...) bias and poor practice in research methodology and data analysis ; 2) lack of transparency in scientific assessment and regulation of the research; 3) long-term denial of weaknesses in cross-species translation; 4) profit-driven motives overriding patient interests; 5) lack of accountability of expenditure on animal research; 6) reductionist-materialism in science which tends to dictate scientific inquiry and control the direction of funding in biomedical research.SummaryBias in animal research needs to be addressed before medical research and healthcare decision-making can be more evidence-based. Research funding may be misdirected on studying 'disease mechanisms' in animals that cannot be replicated outside tightly controlled laboratory conditions, and without sufficient critical evaluation animal research may divert attention away from avenues of research that hold promise for human health. The potential for harm to patients and trial volunteers from reliance on biased animal data1 requires measures to improve its conduct, regulation and analysis. This article draws attention to a few of the many forms of bias in animal research that have come to light in the last decade and offers a strategy incorporating ten recommendations stated at the end of each section on bias. The proposals need development through open debate and subsequent rigorous implementation so that reviewers may determine the value of animal research to human health. The 10Rs + are protected by a Creative Commons Attribution 3.0 Unported License and therefore may be 'shared, remixed or built on, even commercially, so long as attributed by giving appropriate credit with a link to the license, and indicate if changes were made.’. (shrink)

Diverse leadership in information and communication technology can be defined as an approach to empower digital innovation. Digital innovation is a key driver of digital and business transformation. This process demands human transformation to complement business transformation in order to achieve long term sustainability. Changing the culture, fostering an inclusive mindset and guaranteeing diversity are challenging yet foundational elements in building a legacy and require inclusive digital ethics leadership. Our society needs to undergo disruptive and transformative changes in (...) order to adapt to exponential technological advances in the educational, professional, cultural and governance fields. The digital era holds great potential for increased inclusion, reduction or even closure of the „Gender Leadership Gap “in all industries, especially in the Construction and IT industry, Research and Development sectors, as well as in Academia where women are still underrepresented. This article analyzes how diverse leadership in ICT is a moral imperative for our society, emphasizes the need to recalibrate and reshape our approach and highlights the benefits of leveraging women’s potential in the social, digital and business transformation arenas. This article aims to fill an ‘ethical vacuum’ brought to light by the emerging technologies` revolution, focuses on both ethical and data governance aspects in digital innovation and offers approaches to answer how diverse leadership can contribute towards avoiding gender bias, optimizing inclusion and further improve crucial digital ethics aspects in data-driven technologies - such as algorithmic bias, fairness, transparency in AI. (shrink)

The article focuses on exploring the directions for implementing innovative analytical-statistical technologies as a tool for monitoring and detecting corruption in the state. To achieve this goal, the author clarifies the content of key concepts, defines the essence of innovative analytical-statistical technologies, and analyzes the applications of these technologies as elements of the state’s anti-corruption policy. It is determined that modern analytical-statistical technologies are integral to information technologies, which have emerged as a separate branch of production known as the information (...) industry. Information technologies, including analytical-statistical ones, are realized through information systems, which combine technical tools (computers, communication devices and channels, peripherals, input/output devices, etc.) with software applications that facilitate the functioning of the technical components and analytical aspects, including the mathematical component (statistics). The use of analytical-statistical technologies as a tool to counter corruption at the state level is driven by the specificity of the corruption identification process. The application of relevant technologies is possible when perceiving corruption as a form of fraud. Therefore, the search for corruption in modern society requires a comprehensive approach, utilizing mathematical, analytical, and psychophysical research methods. This approach enables not only the identification but also the prediction of probable instances of abuse. Five directions for implementing innovative analytical-statistical technologies in anti-corruption activities are identified: 1) automation of data collection and processing; 2) automation of communication and office processes; 3) automation of direct management processes; 4) automation of decision-making processes and development; 5) development of expert systems. It is noted that analytical-statistical technologies find the most implementation in four of the identified directions: data collection/processing, direct management, decision support, and expert support. (shrink)

Strategic management is now essential in the ever-changing digital media ecosystem to fully use creative content ideas. Focussing on how strategic management has driven innovation, this research study traces the development of content strategies in the digital media business. Successful content production methods are identified by analysing significant trends, technology, and business models in the research. Content creation and delivery have been revolutionised by digital platforms, data analytics, and audience interaction strategies. This study delves into these changes. (...) In order to provide light on best practices and new initiatives, it also analyses case studies of prominent digital media firms. According to the results, companies that want to stay competitive and successfully engage audiences in the digital age need to have a proactive and adaptable strategy management approach. Policymakers, academics, and professionals in the digital media sector may all benefit from this study's findings by better comprehending the factors that drive content creation and strategic management. (shrink)

The widely accepted two-dimensional circumplex model of emotions posits that most instances of human emotional experience can be understood within the two general dimensions of valence and activation. Currently, this model is facing some criticism, because complex emotions in particular are hard to define within only these two general dimensions. The present theory-driven study introduces an innovative analytical approach working in a way other than the conventional, two-dimensional paradigm. The main goal was to map and project semantic emotion space (...) in terms of mutual positions of various emotion prototypical categories. Participants (N = 187; 54.5% females) judged 16 discrete emotions in terms of valence, intensity, controllability and utility. The results revealed that these four dimensional input measures were uncorrelated. This implies that valence, intensity, controllability and utility represented clearly different qualities of discrete emotions in the judgments of the participants. Based on this data, we constructed a 3D hypercube-projection and compared it with various two-dimensional projections. This contrasting enabled us to detect several sources of bias when working with the traditional, two-dimensional analytical approach. Contrasting two-dimensional and three-dimensional projections revealed that the 2D models provided biased insights about how emotions are conceptually related to one another along multiple dimensions. The results of the present study point out the reductionist nature of the two-dimensional paradigm in the psychological theory of emotions and challenge the widely accepted circumplex model. (shrink)

Background The rapidly growing quantity of health data presents researchers with ample opportunity for innovation. At the same time, exploitation of the value of Big Data poses various ethical challenges that must be addressed in order to fulfil the requirements of responsible research and innovation (Gerke et al. 2020 ; Howe III and Elenberg 2020 ). Data sovereignty and its principles of self-determination and informed consent are central goals in this endeavor. However, their consistent implementation (...) has enormous consequences for the collection and processing of data in practice, especially given the complexity and growth of data in healthcare, which implies that artificial intelligence (AI) will increasingly be applied in the field due to its potential to unlock relevant, but previously hidden, information from the growing number of data (Jiang et al. 2017 ). Consequently, there is a need for ethically sound guidelines to help determine how data sovereignty and informed consent can be implemented in clinical research. Methods Using the method of a narrative literature review combined with a design thinking approach, this paper aims to contribute to the literature by answering the following research question: What are the practical requirements for the thorough implementation of data sovereignty and informed consent in healthcare? Results We show that privacy-preserving technologies, human-centered usability and interaction design, explainable and trustworthy AI, user acceptance and trust, patient involvement, and effective legislation are key requirements for data sovereignty and self-determination in clinical research. We outline the implications for the development of IT solutions in the German healthcare system. (shrink)

This article examines how the availability of Big Data, coupled with new data analytics, challenges established epistemologies across the sciences, social sciences and humanities, and assesses the extent to which they are engendering paradigm shifts across multiple disciplines. In particular, it critically explores new forms of empiricism that declare ‘the end of theory’, the creation of data-driven rather than knowledge-driven science, and the development of digital humanities and computational social sciences that propose radically different ways (...) to make sense of culture, history, economy and society. It is argued that: Big Data and new data analytics are disruptive innovations which are reconfiguring in many instances how research is conducted; and there is an urgent need for wider critical reflection within the academy on the epistemological implications of the unfolding data revolution, a task that has barely begun to be tackled despite the rapid changes in research practices presently taking place. After critically reviewing emerging epistemological positions, it is contended that a potentially fruitful approach would be the development of a situated, reflexive and contextually nuanced epistemology. (shrink)

This research examines whether and how firms can meet both business goals and social needs through their innovation activities. We examine antecedents and consequences of innovation that addresses social needs, in addition to business goals, using data collected from European for-profit firms. We find that innovation including social intent is more likely under conditions of high market turbulence, which represents an important form of demand-driven threats. Meanwhile, we find no relationship with competitive intensity, a form (...) of pressure driven threats. Together, these findings suggest that customers and other stakeholders are more likely to drive firms to focus on the social dimension than competitors. The findings also indicate that innovation including social intent is positively related with customer acceptance, which supports the notion that innovation can meet both business goals and social needs. This relationship is partially mediated by perceived market turbulence, which highlights the importance of customers and their demands for social responsibility. This research advances both theory and practice as we add to existing discourses on innovation by providing a broader than common perspective that includes the social dimension as a potential part of innovation conducted to meet business goals. Furthermore, we shed light on how and when firms are likely to include intended social outcomes in their innovation and when they are less likely to do so, which highlights a potential untapped opportunity. (shrink)

Under the innovation-driven development strategy, the improvement of the core competitiveness of enterprises demonstrates increasing dependence on the ability of technological innovation. In this article, data of A-share listed companies in Shanghai and Shenzhen stock markets from 2008 to 2018 were selected as research samples for the analysis of the influencing factors and mechanism of enterprise technological innovation from the dual perspectives of the external economic environment and internal management system based on the use of (...) the fixed-effect model. The results show that government intervention significantly hinders enterprises' investment in resources for technological innovation, and less government intervention can improve the innovation investment of enterprises. The intervention of internal control fails to bring institutional advantages; rather, it aggravates the negative effect of government intervention on enterprise technological innovation. The research enriches the existing academic research results on government intervention, internal control quality, and enterprise technological innovation. The findings provide experience for accelerating the marketization process in China, enterprise governance, and improving the level of enterprise technological innovation. (shrink)

Participant-driven research is a burgeoning domain of research innovation, often facilitated by mobile technologies. Return of results and data are common hallmarks, grounded in transparency and data democracy. PDR has much to teach traditional research about these practices and successful engagement. Recommendations calling for new state laws governing research with mHealth modalities common in PDR and federal creation of review mechanisms, threaten to stifle valuable participant-driven innovation, including in return of results.

Purpose This study advances a reconceptualization of data and information which overcomes normative understandings often contained in data policies at national and international levels. This study aims to propose a conceptual framework that moves beyond subject- and collective-centric normative understandings. Design/methodology/approach To do so, this study discusses the European Union (EU) and China’s approaches to data-driven technologies highlighting their similarities and differences when it comes to the vision underpinning how tech innovation is shaped. Findings Regardless (...) of the different attention to the subject (the EU) and the collective (China), the normative understandings of technology by both actors remain trapped into a positivist approach that overlooks all that is not and cannot be turned into data, thus hindering the elaboration of a more holistic ecological thinking merging humans and technologies. Originality/value Revising the philosophical and political debate on data and data-driven technologies, a third way is elaborated, i.e. federated data as commons. This third way puts the subject as part by default of a collective at the centre of discussion. This framing can serve as the basis for elaborating sociotechnical alternatives when it comes to define and regulate the mash-up of humans and technology. (shrink)

Improving the coordination between technological innovation in high-tech industries and regional economic development is an important measure for all provinces to implement the innovation-driven development strategy. Based on the analysis of the mechanism of high-tech industrial technological innovation and regional economic development, this paper constructs the measurement index system of high-tech industrial technological innovation and regional economic development, and the chain network DEA model, entropy weight method, coupling coordination model, and exploratory spatial data analysis (...) technology are comprehensively used to empirically analyze the technological innovation efficiency of high-tech industries, regional economic development level, and the coupling coordination relationship between them and the spatiotemporal evolution trend in 24 provinces of China from 2009 to 2018. The results show that there are significant differences in the average level of technological innovation efficiency of high-tech industry in each stage, which is the most prominent in the technology research and development stage, followed by the industrialization stage, and finally in the technology transformation stage. The degree of coupling and coordination between high-tech industrial technological innovation and regional economic development is increasing year by year, which is in the adaptation stage of high-tech industrial technological innovation and regional economic development. From the perspective of spatial pattern, there is a positive spatial autocorrelation between the coupling coordination degree of high-tech industrial technological innovation and regional economic development. The number of provinces with low coupling coordination degree is much larger than that with high coupling coordination degree, showing a serious imbalance of regional heterogeneity and forming two spatial aggregation areas of “high-high” diffusion effect and “low-low” low-speed growth. (shrink)

This paper conducts a comparative analysis of data governance mechanisms concerning the secondary use of health data in Taiwan and the European Union (EU). Both regions have adopted distinctive approaches and regulations for utilizing health data beyond primary care, encompassing areas such as medical research and healthcare system enhancement. Through an examination of these models, this study seeks to elucidate the strategies, frameworks, and legal structures employed by Taiwan and the EU to strike a delicate balance between (...) the imperative of data-driven healthcare innovation and the safeguarding of individual privacy rights. This paper examines and compares several key aspects of the secondary use of health data in Taiwan and the EU. These aspects include data governance frameworks, legal and regulatory frameworks, data access and sharing mechanisms, and privacy and security considerations. This comparative exploration offers invaluable insights into the evolving global landscape of health data governance. It provides a deeper understanding of the strategies implemented by these regions to harness the potential of health data while upholding the ethical and legal considerations surrounding its secondary use. The findings aim to inform best practices for responsible and effective health data utilization, particularly in the context of medical AI applications. (shrink)

Since the emergence of the innovative field of artificial intelligence in the 1960s, the late Hubert Dreyfus insisted on the ontological distinction between man and machine, human and artificial intelligence. In the different editions of his classic and influential book What computers can’t do, he posits that an algorithmic machine can never fully simulate the complex functioning of the human mind—not now, nor in the future. Dreyfus’ categorical distinctions between man and machine are still relevant today, but their relation has (...) become more complex in our increasingly data-driven society. We, humans, are continuously immersed within a technological universe, while at the same time ubiquitous computing, in the words of computer scientist Mark Weiser, “forces computers to live out here in the world with people” The cell phone reader. Peter Lang Publishing, New York, 2006, p 20). Dreyfus’ ideas are therefore challenged by thinkers such as Weiser, Kevin Kelly, Bruno Latour, Philip Agre, and Peter Paul Verbeek, who all argue that humans are much more intrinsically linked to machines than the original dichotomy suggests—they have evolved in concert. Through a discussion of the classical concepts of individuum and ‘authenticity’ within Western civilization, this paper argues that within the ever-expanding data-sphere of the twenty-first century, a new concept of man as ‘aggregate of data’ has emerged, which further erodes and undermines the categorical distinction between man and machine. This raises political and ethical questions beyond the confines of technology and artificial intelligence. Moreover, this seemingly never-ending debate on what computers should do provokes the philosophical necessity to once again define the concept of what it is to be ‘human.’. (shrink)

DNA methylation plays a key role in neural cell fate and provides a molecular link between early life stress and later-life behavioral phenotypes. Here, studies that combine neuroimaging methods and DNA methylation analysis in pediatric population with a history of adverse experiences were systematically reviewed focusing on: targeted genes and neural correlates; statistical models used to examine the link between DNA methylation and neuroimaging data also considering early life stress and behavioral outcomes. We identified 8 studies that report associations (...) between DNA methylation and brain structure/functions in infants, school age children and adolescents faced with early life stress condition. Results showed that several genes were investigated and different neuroimaging techniques were performed. Statistical model used ranged from correlational to more complex moderated mediation models. Most of the studies considered DNA methylation and neural correlates as mediators in the relationship between early life stress and behavioral phenotypes. Understanding what role DNA methylation and neural correlates play in interaction with early life stress and behavioral outcomes is crucial to promote theory-driven studies as the future direction of this research fields. (shrink)

Philosophers of science diverge on the question what drives the growth of scientific knowledge. Most of the twentieth century was dominated by the notion that theories propel that growth whereas experiments play secondary roles of operating within the theoretical framework or testing theoretical predictions. New experimentalism, a school of thought pioneered by Ian Hacking in the early 1980s, challenged this view by arguing that theory-free exploratory experimentation may in many cases effectively probe nature and potentially spawn higher evidence-based theories. Because (...) theories are often powerless to envisage workings of complex biological systems, theory-independent experimentation is common in the life sciences. Some such experiments are triggered by compelling observation, others are prompted by innovative techniques or instruments, whereas different investigations query big data to identify regularities and underlying organizing principles. A distinct fourth type of experiments is motivated by a major question. Here I describe two question-guided experimental discoveries in biochemistry: the cyclic adenosine monophosphate mediator of hormone action and the ubiquitin-mediated system of protein degradation. Lacking underlying theories, antecedent data bases, or new techniques, the sole guides of the two discoveries were respective substantial questions. Both research projects were similarly instigated by theory-free exploratory experimentation and continued in alternating phases of results-based interim working hypotheses, their examination by experiment, provisional hypotheses again, and so on. These two cases designate theory-free, question-guided, stepwise biochemical investigations as a distinct subtype of the new experimentalism mode of scientific enquiry. (shrink)

Innovative, data-driven methods provide more rigorous and systematic evidence for the description and explanation of diachronic semantic processes. The volume systematises, reviews, and promotes a range of empirical research techniques and theoretical perspectives that currently inform work across the discipline of historical semantics. In addition to emphasising the use of new technology, the potential of current theoretical models (e.g. within variationist, sociolinguistic or cognitive frameworks) is explored along the way.

Data analytics and data-driven approaches in Machine Learning are now among the most hailed computing technologies in many industrial domains. One major application is predictive analytics, which is used to predict sensitive attributes, future behavior, or cost, risk and utility functions associated with target groups or individuals based on large sets of behavioral and usage data. This paper stresses the severe ethical and data protection implications of predictive analytics if it is used to predict sensitive (...) information about single individuals or treat individuals differently based on the data many unrelated individuals provided. To tackle these concerns in an applied ethics, first, the paper introduces the concept of “predictive privacy” to formulate an ethical principle protecting individuals and groups against differential treatment based on Machine Learning and Big Data analytics. Secondly, it analyses the typical data processing cycle of predictive systems to provide a step-by-step discussion of ethical implications, locating occurrences of predictive privacy violations. Thirdly, the paper sheds light on what is qualitatively new in the way predictive analytics challenges ethical principles such as human dignity and the (liberal) notion of individual privacy. These new challenges arise when predictive systems transform statistical inferences, which provide knowledge about the cohort of training data donors, into individual predictions, thereby crossing what I call the “prediction gap”. Finally, the paper summarizes that data protection in the age of predictive analytics is a collective matter as we face situations where an individual’s (or group’s) privacy is violated using dataotherindividuals provide about themselves, possibly even anonymously. (shrink)

The fossil record is paleontology’s great resource, telling us virtually everything we know about the past history of life. This record, which has been accumulating since the beginning of paleontology as a professional discipline in the early nineteenth century, is a collection of objects. The fossil record exists literally, in the specimen drawers where fossils are kept, and figuratively, in the illustrations and records of fossils compiled in paleontological atlases and compendia. However, as has become increasingly clear since the later (...) twentieth century, the fossil record is also a record of data. Paleontologists now routinely abstract information from the physical fossil record to construct databases that serve as the basis for quantitative analysis of patterns in the history of life. What is the significance of this distinction? While it is often assumed that the orientation towards treating the fossil record as a record of data is an innovation of the computer age, it turns out that nineteenth century paleontology was substantially “data driven.” This paper traces the evolution of data practices and analyses in paleontology, primarily through examination of the compendia in which the fossil record has been recorded over the past 200 years. I argue that the transition towards conceptualizing the fossil record as a record of data began long before the emergence of the technologies associated with modern databases (such as digital computers and modern statistical methods). I will also argue that this history reveals how new forms of visual representation were associated with the transition from seeing the fossil record as a record of objects to one of data or information, which allowed paleontologists to make new visual arguments about their data. While these practices and techniques have become increasingly sophisticated in recent decades, I will show that their basic methodology was in place over a century ago, and that, in a sense, paleontology has always been a “data driven” science. (shrink)

This Open Access book shows how value sensitive design (VSD), responsible innovation, and comprehensive engineering can guide the rapid development of technological responses to the COVID-19 crisis. Responding to the ethical challenges of data-driven technologies and other tools requires thinking about values in the context of a pandemic as well as in a post-COVID world. Instilling values must be prioritized from the beginning, not only in the emergency response to the pandemic, but in how to proceed with (...) new societal precedents materializing, new norms of health surveillance, and new public health requirements. -/- The contributors with expertise in VSD bridge the gap between ethical acceptability and social acceptance. By addressing ethical acceptability and societal acceptance together, VSD guides COVID-technologies in a way that strengthens their ability to fight the virus, and outlines pathways for the resolution of moral dilemmas. This volume provides diachronic reflections on the crisis response to address long-term moral consequences in light of the post-pandemic future. Both contact-tracing apps and immunity passports must work in a multi-system environment, and will be required to succeed alongside institutions, incentive structures, regulatory bodies, and current legislation. This text appeals to students, researchers and importantly, professionals in the field. (shrink)

This contribution discusses the development of the Data Ethics Decision Aid (DEDA), a framework for reviewing government data projects that considers their social impact, the embedded values and the government’s responsibilities in times of data-driven public management. Drawing from distinct qualitative research approaches, the DEDA framework was developed in an iterative process (2016–2018) and has since then been applied by various Dutch municipalities, the Association of Dutch Municipalities, and the Ministry of General Affairs (NL). We present (...) the DEDA framework as an effective process to moderate case-deliberation and advance the development of responsible data practices. In addition, by thoroughly documenting the deliberation process, the DEDA framework establishes accountability. First, this paper sheds light on the necessity for data ethical case deliberation. Second, it describes the prototypes, the final design of the framework, and its evaluation. After a comparison with other frameworks, and a discussion of the findings, the paper concludes by arguing that the DEDA framework is a useful process for ethical evaluation of data projects for public management and an effective tool for creating awareness of ethical issues in data practices. (shrink)

AI-driven journalism refers to various methods and tools for gathering, verifying, producing, and distributing news information. Their potential is to extend human capabilities and create new forms of augmented journalism. Although scholars agreed on the necessity to embed journalistic values in these systems to make AI systems accountable, less attention was paid to data quality, while the results’ accuracy and efficiency depend on high-quality data in any machine learning task. Assessing data quality in the context of (...) AI-driven journalism requires a broader and interdisciplinary approach, relying on the challenges of data quality in machine learning and the ethical challenges of using machine learning in journalism. To better identify these, we propose a data quality assessment framework to support the collection and pre-processing stages in machine learning. It relies on three of the core principles of ethical journalism—accuracy, fairness, and transparency—and participates in the shift from model-centric to data-centric AI, by focusing on data quality to reduce reliance on large datasets with errors, making data labelling consistent, and better integrating journalistic knowledge. (shrink)

The ‘smart city’ has been driven by advances in information and communication technologies, with the aim of integrating these technologies with urban infrastructures for improved optimisation, automation and control. Smart cities have emerged as a response to the challenges faced by megacities and are likely to manifest the ‘datafying’ society in the public space. However, the pervasive nature of data collection, continuous analysis and inference, and long-term data storage result in a potentially problematic reconfiguration of society that (...) undermines individual and collective autonomy. This article proposes the concept of ‘data autonomy’ as a guiding principle for the development of smart cities, addressing challenges related to the potential misuse of personal data, sphere transgression, and datafied gentrification. Section II. explores concerns related to data protection law, which can be understood as a proxy for the relationship between the individual and society, highlighting power dynamics and the autonomy of individuals and communities. Section III. discusses the concept of sphere transgression, where tech giants expand their influence in society without adequate expertise or accountability. Section IV. examines the potential impact of datafied gentrification on individual and collective autonomy. Building on the challenges identified throughout these sections, this paper proposes ‘data autonomy’ as a guiding principle for smart city development in section V., promoting a value-based approach centred on human dignity. To make it practicable, increased interdisciplinary collaboration in the development of smart cities is needed, with a particular focus on improved design and impact assessment methodologies. (shrink)

The use of Artificial Intelligence (AI) in educational gamification marks a significant advancement, transforming traditional learning methods by offering interactive, adaptive, and personalized content. This approach makes historical content more relatable and promotes active learning and exploration. This research presents an innovative approach to heritage education, combining AI and gamification, explicitly targeting the Silk Roads. It represents a significant progression in a series of research, transitioning from basic 2D textual interactions to a 3D environment using photogrammetry, combining historical authenticity and (...) immersive gameplay. It features AI-driven characters developed in Unreal Engine, which provides heritage insights augmented by personalized interactions, highlighting the importance of collaborative tools like interactive quizzes for enriching education and promoting critical thinking and community among learners. In conclusion, this research underlines the transformative impact of AI and gamification in heritage education enhancement, integrating real-time voice conversation and interactive pedagogical methods into immersive 3D environments as a supplementary tool. (shrink)

AbstractAdvances in precision agriculture, driven by big data technologies and machine learning algorithms can transform agriculture by enhancing crop and livestock productivity and supporting faster and more accurate on and off-farm decision making. However, little is known about how PA can influence farmers’ sense of self, their skills and competencies, and the meanings that farmers ascribe to farming. This study is animated by scholarly commitment to social identity research, and draws from socio-cyber-physical systems research, domestication theory, and activity (...) theory. This conceptualization of PA within these theoretical perspectives helps to render visible how big agricultural data and machine learning algorithms can affect meaning, doing, and being for US farmers. Through analysis of data from six focus group discussions and follow-up surveys with stakeholders across the PA value chain, this paper shows that PA tools can necessitate farmers to learn and develop new competencies such as flying drones and interpreting yield maps. At the same time, PA can shape new meaning of farm work and new expectation about a ‘good farmer’, changing what it means to be a ‘successful’ farmer from someone who is not only a data observer or data gatherer but also validators of PA models by using their local knowledge of agronomic and environmental phenomenon. We conclude that PA can alter social expectations about farming by reorienting the role of farmers. Policymakers and agriculture extension and outreach programmers can develop more socially relevant PA knowledge and innovation if they can attend to both new and traditional ‘good farmer’ identities. (shrink)

The rapid urbanization of modern cities necessitates innovative approaches to data collection and integration for smarter urban management. With the Internet of Things (IoT) at the core of these advancements, the ability to efficiently gather, analyze, and utilize data becomes paramount. Generative Artificial Intelligence (AI) is revolutionizing data collection by enabling intelligent synthesis, anomaly detection, and real-time decision-making across interconnected systems. This paper explores how generative AI enhances IoT-driven data collection in smart cities, focusing on (...) applications in transportation, energy, public safety, and environmental monitoring. By addressing challenges such as data privacy, scalability, and ethical considerations, the study highlights how generative AI transforms urban governance and paves the way for sustainable and citizen-centric development. Key trends, case studies, and future research directions are discussed, showcasing the potential of generative AI as a cornerstone of smart city initiatives. (shrink)

Deep learning is widely used in the fields of information technology and education innovation but there are few studies for young children in the preschool stage. Therefore, we aimed to explore factors that affect children’s learning ability through collecting relevant information from teachers in the kindergarten. Literature review, interview, and questionnaire survey methods were used to determine the influencing factors of deep learning. There were five dimensions for these factors: the level of difficulty of academic, communication skills, level of (...) active collaboration, level of in-depth processing, and reflection level evaluation. Reliability and validity tests were used to analyze the data from questionnaires. In total, 100 valid questionnaires were collected. The Cronbach coefficients for academic challenge, communication, active cooperation, deep processing, and reflective evaluation were 0.801, 0.689, 0.770, 0.758, and 0.665, respectively. Principal component analysis revealed that there were three main factors that affect children’s learning depth: the level of deep processing, the level of reflective evaluation, and the active level of collaboration. In conclusion, there were several factors affecting deep learning in children and further studies are warranted to promote the development of this field. (shrink)

How can digitised assets of Galleries, Libraries, Archives and Museums be reused to unlock new value? What are the implications of viewing large-scale cultural heritage data as an economic resource, to build new products and services upon? Drawing upon valuation studies, we reflect on both the theory and practicalities of using mass-digitised heritage content as an economic driver, stressing the need to consider the complexity of commercial-based outcomes within the context of cultural and creative industries. However, we also problematise (...) the act of considering such heritage content as a resource to be exploited for economic growth, in order to inform how we consider, develop, deliver and value mass-digitisation. Our research will be of interest to those wishing to understand a rapidly changing research and innovation landscape, those considering how to engage memory institutions in data-driven activities and those critically evaluating years of mass-digitisation across the heritage sector. (shrink)

There is a long history, dating back to the 50s, which examines the manner in which team roles contribute to effective team performance. However, much of this work has been built on ad-hoc teams working together for short periods of time under conditions of minimal stress. Additionally, research has been conducted with little attention paid to the importance of temporal factors, despite repeated calls for the importance of considering time in team research (e.g., Mohammed, Hamilton, & Lim, 2009). To begin (...) to understand team roles and how temporal aspects may impact the types of team roles employed when teams are working in extreme mission critical environments, the current manuscript uses a data-driven, bottom-up approach. Specifically, we employ the use of retrospective historical data as our input and a historiometric approach (Simonton, 2003). Source documents consist primarily of autobiographies, memoires, biographies, and first-hand accounts of crew interaction during spaceflight. Critical incidents regarding team interaction were extracted from these source documents and independently coded for team roles by two trained raters. Results of the study speak to the importance of task and social roles within teams that are predominantly intact and operating in extreme environments where mistakes can be life threatening. Evidence for the following task (i.e., coordinator, boundary spanner, team leader, evaluator, critic, information provider, team player, and innovator) and social roles (i.e., team builder, nurturer, harmonizer, entertainer, jokester, and the negative roles of attention seeker and negativist) were found. While it is often task roles that receive the greatest attention, results point to the importance of not neglecting the socioemotional health of the team (and the corresponding roles). Results also indicated that while some roles were consistently enacted independent of temporal considerations (e.g., mission length), the degree to which others were enacted varied across missions of differing lengths. Additionally, based on the current sample we see the following trends: (1) increased enactment of the team builder role as mission duration increases, (2) prominence of the entertainer role, and (3) increased emphasis on the visionary/problem solver role on missions over two years. (shrink)

Research on language evolution is an established subject area yet permeated by terminological controversies about which topics should be considered pertinent to the field and which not. By consequence, scholars focusing on language evolution struggle in providing precise demarcations of the discipline, where even the very central notions of evolution and language are elusive. We aimed at providing a data-driven characterisation of language evolution as a field of research by relying on quantitative analysis of data drawn from (...) 697 reviews on 255 submissions from the Joint Conference on Language Evolution 2022 (Kanazawa, Japan). Our results delineate a field characterized by a core of main research topics such as iconicity, sign language, multimodality. Despite being explored within the framework of language evolution research, only very recently these topics became popular in linguistics. As a result, language evolution has the potential to emerge as a forefront of linguistic research, bringing innovation to the study of language. We also see the emergence of more recent topics like rhythm, music, and vocal learning. Furthermore, the community identifies cognitive science, primatology, archaeology, palaeoanthropology, and genetics as key areas, encouraging empirical rather than theoretical work. With new themes, models, and methodologies emerging, our results depict an intrinsically multidisciplinary and evolving research field, likely adapting as language itself. (shrink)

Public administrations, including national government agencies as well as the EU institutions, face not only structural changes, but are increasingly talking about the need to transform management, foster innovation, and share responsibilities. The role of AI is the key composite element of this process. However, not only from the technological point of view. AI also needs the human “perspective”. It is very important to include the so-called soft components (institutional culture, inter-cultural awareness, and organizational needs) to facilitate the mental (...) shift and thus the buy-in. Technological advances in the area of AI inevitably result in further change and this alteration has to be managed. A need to break silo mentality moves organizations towards matrix-based structures with advanced reporting relationships. With the focus on the people side of change and the boundaries of the data-driven decision-making, this study provides insights in the correlations between AI as a tool and change process in public administrations. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:Reviewed by:History of Philosophy and the Reflective Society by Riccardo PozzoRobert R. ClewisPOZZO, Riccardo. History of Philosophy and the Reflective Society. Berlin: Walter de Gruyter, 2021. vi + 231 pp. Cloth, $94.99In a forward-looking proposal, Pozzo lays out his vision for a multidisciplinary history of philosophy "from a global perspective." This book is "a long position paper, an extended essay dedicated to twenty-first century policies of philosophical research from (...) a global perspective." The chosen format is a Denkschrift, that is, a memorandum in the sense of a document that experts lay out for their government to ponder. Perhaps, Pozzo suggests, the history of philosophy could even have an impact on economic authorities and policymakers. Indeed, the work seems driven by the laudable aim of promoting access and equality around the world. The author reveals a just concern for migrants in particular, since they bring [End Page 156] with them their intellectual traditions and philosophical texts, which can be translated and thereby foster intercultural dialogue.The book consists of an introduction (chapter 1) followed by nine chapters. It has a thirty-page references list as well as subject and name indexes. In part one, Pozzo argues for the need for new narratives in the history of philosophy (chapter 2), in order to establish its role in addressing migration (chapter 3). The second part concerns reflective society (chapter 4). It considers societal readiness (chapter 5) and cultural innovation (chapter 6). Part three explores the paradigm shift from a close reading of texts to a "distant" reading of corpora (chapter 7), a shift Pozzo endorses. It examines implications for the translation of languages (chapter 8) and the translation of studies (knowledge) and competencies (chapter 9). Chapter 10 concludes by briefly reflecting on the notions of the "fluidity" of peoples and their ideas, biodiversity (and food), and spiritual humanism based on dignity.Scholars in the history of philosophy "should be open to the idea of a pluriversum of a history of facts and places, whereby history of philosophy finds its grounding in spatial heterogeneity." Nowadays, when people migrate and transfer competencies at increasing velocity, the "history of philosophy can be usefully reinvented on the basis of its consideration of intercultural dialogue." Pozzo considers the World Digital Library to be "one of the most exciting examples of corpora that talk to each other"—an idea he uses not merely metaphorically. By "corpora" is meant not just the sum of separate books, but XML-accessible complete collections of traditions of texts, with corresponding dictionaries, thesauri, and reference works, which make it possible to analyze a number of original texts as well as transliterations into other alphabets or hanzi characters. The result would be a "data-driven" history of philosophy.Given that history of philosophy is a product of culture, it is influenced by migrating peoples, who bring philosophical ideas and religious traditions with them. Research on migration thus ought to also consider translations of texts and competencies from one to another context, be it linguistic, economic, political, or cultural. The humanities, and a reflective society more generally, might provide a partial basis for intercultural dialogue. The history of philosophy might even thereby improve the quality of life of citizens.Pozzo rightly emphasizes that we need multilingual philosophers and historians of philosophy. History of philosophy ought to also be based on robust lexical and historical considerations, and historians of philosophy should be trained in a number of the world's languages as well as face the challenge of dealing with multilingualism and with validating translations. Pozzo's proposed methodology, informed by computational techniques, relies on tools such as vocabularies, ontologies, concordances, frequencies, or the analysis of texts and corpora, which integrates quantitative and formal methods into the portfolio of the methods of history of philosophy and intellectual history. Since Pozzo has published [End Page 157] widely on Kant, it is fitting that he explores Kant on migration as well as the editorial contexts and translation projects concerning the philosopher's writings.As for aesthetics, Pozzo notes that the history of philosophy can reflect on migrants' cultural and artistic creations. Historians of philosophy should thus start observing and interpreting phenomena created by... (shrink)

Epidemiology is a field torn between practices of surveillance and methods of analysis. Since the onset of COVID-19, epidemiological expertise has been mostly identified with the first, as dashboards of case and mortality rates took centre stage. However, since its establishment as an academic field in the early 20th century, epidemiology’s methods have always impacted on how diseases are classified, how knowledge is collected, and what kind of knowledge was considered worth keeping and analysing. Recent advances in digital epidemiology, this (...) article argues, are not just a quantitative expansion of epidemiology’s scope, but a qualitative extension of its analytical traditions. Digital epidemiology is enabled by deep and digital phenotyping, the large-scale re-purposing of any data scraped from the digital exhaust of human behaviour and social interaction. This technological innovation is in need of critical examination, as it poses a significant epistemic shift to the production of pathological knowledge. This article offers a critical revision of the key literature in this budding field to underline the extent to which digital epidemiology is envisioned to redefine the classification and understanding of disease from the ground up. Utilising analytical tools from science and technology studies, the article demonstrates the disruptive expectations built into this expansion of epidemiological surveillance. Given the sweeping claims and the radical visions articulated in the field, the article develops a tentative critique of what I call a fantasy of pathological omniscience; a vision of how data-driven engineering seeks to capture and resolve illness in the world, past, present and future. (shrink)

Insurance markets have always relied on large amounts of data to assess risks and price their products. New data-driven technologies, including wearable health trackers, smartphone sensors, predictive modelling and Big Data analytics, are challenging these established practices. In tracking insurance clients’ behaviour, these innovations promise the reduction of insurance costs and more accurate pricing through the personalisation of premiums and products. Building on insights from the sociology of markets and Science and Technology Studies, this article investigates (...) the role of economic experimentation in the making of data-driven personalisation markets in insurance. We document a case study of a car insurance experiment, launched by a Belgian direct insurance company in 2016 to set up an experiment of tracking driving style behavioural data of over 5000 participants over a one-year period. Based on interviews and document analysis, we outline how this in vivo experiment was set-up, which interventions and manipulations were imposed to make the experiment successful, and how the study was evaluated by the actors. Using JL Austin’s distinction between happy and unhappy statements, we argue how the experiment, despite its failure not to provide the desired evidence, could be considered a ‘happy’ event. We conclude by highlighting the role of economic experiments ‘in the wild’ for the making of future markets of data-driven personalisation. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:Guest Editors' NoteKevin Taylor (bio) and Johnathan Flowers (bio)Welcome to this special fall 2021 issue of Education & Culture. we are pleased to bring you the second installment of this special three-part issue on Deweyan approaches to contemporary issues at the intersection of data and technology.In his extensive writings on philosophy and technology, Luciano Floridi has argued that "the time has come to translate environmental ethics into terms (...) of infosphere and informational entities, for the land we now inhabit is not just the earth."1 This may be appropriate from a Deweyan perspective as Dewey's approach to aesthetic experience focuses on the living organism within its environment and we have become digital humans in digital environments. Our digital world feels unstable because it is in constant change, but Dewey reminds us that "all interactions that effect stability and order in the whirling flux of change are rhythms. There is ebb and flow, systole and diastole: ordered change."2 The rhythms of our digital world pervade all experience in what he termed "doing and undergoing." We are swimming in a sea of data and leaving behind what investigative reporter Julia Angwin terms data pollution.3 But in the data-driven world, that data pollution is collected, analyzed, and monetized by big data tech firms in a kind of societal harm that threatens digital democracy.Whereas part one saw contributions from five scholars throwing curriculum and technology into sharp relief, this issue asks questions such as "Where ought we to begin?", "What does feminist pragmatism have to tell us about the pandemic and online teaching and learning?", and "Do the aims and means of data and technology align with human experience?"John Machielsen's "Responsible Imagineering: John Dewey's Pluralistic Ethics and Technological Innovation" seems like a good place to begin. Machielsen argues for placing Dewey's ethics at the starting point from which technological innovation can flourish. Driven by Deweyan concepts of intelligence, education, and imagination, Machielsen explores, among other themes, socially disruptive technology from the perspective of pragmatists who constantly reconstruct concepts and values through the process of Deweyan inquiry. Human beings are imaginative and plastic with regard to problem solving, and what is needed here is to keep the barriers between technology and everyday life porous so that we do not risk compartmentalizing them as distinctly different realms.In the next two essays, we turn our attention to the growing concerns surrounding intelligent technology in online educational environments. Great strides have been made in the field of artificial intelligence, and intelligent technology has inserted itself into various aspects of data and technology, such as machine learning that creates personalized learning paths for students. Whether these technologies fall under the [End Page 1] category of algorithms, machine learning, or artificial intelligence, they raise questions about their potential impact on teaching and learning.Olivier Del Fabbro is also suspicious of the current state of intelligent technology in his article, "How to Teach Machines in Artificial Intelligence: Technical Education in John Dewey, Gilbert Simondon, and Machine Learning." Citing the Google machine learning model that classified African Americans as gorillas, Del Fabbro utilizes Dewey and Gilbert Simondon to argue that the human–machine relationship is still immature and at risk of creating an alienated situation out of undesirable bad habits. Intelligent technology requires constant care by engineers and programmers to form good habits and adapt to social life if their aims are to align with Deweyan pedagogy.As Del Fabbro focuses on intelligent technology, the human dimension of online learning comes under scrutiny in two articles that adopt feminist pragmatist methods. Krissah Marga B. Taganas's article, "Feminist-Pragmatist Perspectives on Online Education: Challenging and Changing Online Educational Conversation," rightfully acknowledges how the pandemic has magnified existing inequalities in society. The rapid migration to online learning was necessarily expedited in the face of uncertainties surrounding public health, but how do educators migrate classes that were not designed with online learning in mind to begin with? Dewey emphasized schools as sites and tools for change, but how does online education provide an optimal social environment for learning? Taganas utilizes feminist pragmatism as an important philosophical resource to... (shrink)

BackgroundRandomized controlled trials (RCTs) are often complex and expensive to perform. Less than one third achieve planned recruitment targets, follow-up can be labor-intensive, and many have limited real-world generalizability. Designs for RCTs conducted using cohorts and routinely collected health data, including registries, electronic health records, and administrative databases, have been proposed to address these challenges and are being rapidly adopted. These designs, however, are relatively recent innovations, and published RCT reports often do not describe important aspects of their methodology (...) in a standardized way. Our objective is to extend the Consolidated Standards of Reporting Trials (CONSORT) statement with a consensus-driven reporting guideline for RCTs using cohorts and routinely collected health data.MethodsThe development of this CONSORT extension will consist of five phases. Phase 1 (completed) consisted of the project launch, including fundraising, the establishment of a research team, and development of a conceptual framework. In phase 2, a systematic review will be performed to identify publications (1) that describe methods or reporting considerations for RCTs conducted using cohorts and routinely collected health data or (2) that are protocols or report results from such RCTs. An initial “long list” of possible modifications to CONSORT checklist items and possible new items for the reporting guideline will be generated based on the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) and The REporting of studies Conducted using Observational Routinely-collected health Data (RECORD) statements. Additional possible modifications and new items will be identified based on the results of the systematic review. Phase 3 will consist of a three-round Delphi exercise with methods and content experts to evaluate the “long list” and generate a “short list” of key items. In phase 4, these items will serve as the basis for an in-person consensus meeting to finalize a core set of items to be included in the reporting guideline and checklist. Phase 5 will involve drafting the checklist and elaboration-explanation documents, and dissemination and implementation of the guideline.DiscussionDevelopment of this CONSORT extension will contribute to more transparent reporting of RCTs conducted using cohorts and routinely collected health data. (shrink)

In projects concerning big data, ethical questions need to be answered during the design process. In this paper the Value Sensitive Design method is applied in the context of data-driven health services aimed at disease prevention. It shows how Value Sensitive Design, with the use of a moral dialogue and an ethical matrix, can support the identification and operationalization of moral values that are at stake in the design of such services. It also shows that using this (...) method can support meeting the requirements of the General Data Protection Regulation. (shrink)

The global market for biologics and biosimilar pharmaceutical products is experiencing rapid expansion, primarily driven by the continuous discovery of new molecules. However, information regarding Latin America’s biological market remains limited.