Cloud computing is rapidly gaining traction in business. It offers businesses online services on demand (such as Gmail, iCloud and Salesforce) and allows them to cut costs on hardware and IT support. This is the first paper in business ethics dealing with this new technology. It analyzes the informational duties of hosting companies that own and operate cloud computing datacenters (e.g., Amazon). It considers the cloud services providers leasing ‘space in the cloud’ from hosting companies (e.g, (...) Dropbox, Salesforce). And it examines the business and private ‘clouders’ using these services. The first part of the paper argues that hosting companies, services providers and clouders have mutual informational (epistemic) obligations to provide and seek information about relevant issues such as consumer privacy, reliability of services, data mining and data ownership. The concept of interlucency is developed as an epistemic virtue governing ethically effective communication. The second part considers potential forms of government restrictions on or proscriptions against the development and use of cloud computing technology. Referring to the concept of technology neutrality, it argues that interference with hosting companies and cloud services providers is hardly ever necessary or justified. It is argued, too, however, that businesses using cloud services (banks, law firms, hospitals etc. storing client data in the cloud, e.g.) will have to follow rather more stringent regulations. (shrink)

The data science era is characterized by data-driven automated decision systems (ADS) enabling, through data analytics and machine learning, automated decisions in many contexts, deeply impacting our lives. As such, their downsides and potential risks are becoming more and more evident: technical solutions, alone, are not sufficient and an interdisciplinary approach is needed. Consequently, ADS should evolve into data-informed ADS, which take humans in the loop in all the data processing steps. Data-informed ADS should (...) deal with data responsibly, guaranteeing nondiscrimination with respect to protected groups of individuals. Nondiscrimination can be characterized in terms of different types of properties, like fairness and diversity. While fairness, i.e., absence of bias against minorities, has been widely investigated in machine learning, only more recently this issue has been tackled by considering all the steps of data processing pipelines at the basis of ADS, from data acquisition to analysis. Additionally, fairness is just one point of view of nondiscrimination to be considered for guaranteeing equity: other issues, like diversity, are raising interest from the scientific community due to their relevance in society. This paper aims at critically surveying how nondiscrimination has been investigated in the context of complex data science pipelines at the basis of data-informed ADS, by focusing on the specific data processing tasks for which nondiscrimination solutions have been proposed. (shrink)

The social impacts of computer technology are often glorified in public discourse, but there is growing concern about its actual effects on society. In this article, we ask: how does “consent” as an analytical framework make visible the social dynamics and power relations in the capture, extraction, and labor of data science knowledge production? We hypothesize that a form of boundary violation in data science workplaces—gender harassment—may correlate with the ways humans’ lived experiences are extracted to produce Big (...) Data. The concept of consent offers a useful way to draw comparisons between gender relations in data science and the means by which machines are trained to learn and reason. Inspired by how Big Tech leaders describe unsupervised machine learning, and the co-optation of “revolutionary” rhetoric they use to do so, we introduce a concept we call “techniques of invisibility.” Techniques of invisibility are the ways in which an extreme imbalance between exposure and opacity, demarcated along fault lines of power, are fabricated and maintained, closing down the possibility for bidirectional transparency in the production and applications of algorithms. Further, techniques of invisibility, which we group into two categories—epistemic injustice and the Brotherhood—include acts of subjection by powerful actors in data science designed to quell resistance to exploitative relations. These techniques may be useful in making further connections between epistemic violence, sexism, and surveillance, sussing out persistent boundary violations in data science to render the social in data science visible, and open to scrutiny and debate. (shrink)

This article introduces an interpretative approach to the analysis of situations in computational settings called situational analytics. I outline the theoretical and methodological underpinnings of this approach, which is still under development, and show how it can be used to surface situations from large data sets derived from online platforms such as YouTube. Situational analytics extends to computationally-mediated settings a qualitative methodology developed by Adele Clarke, Situational Analysis, which uses data mapping to detect heterogeneous entities in fieldwork (...) class='Hi'>data to determine ‘what makes a difference’ in a situation. Situational analytics scales up this methodology to analyse situations latent in computational data sets with semi-automated methods of textual and visual analysis. I discuss how this approach deviates from recent analyses of situations in computational social science, and argue that Clarke’s framework renders tractable a fundamental methodological problem that arises in this area of research: while social researchers turn to computational settings in order to analyse social life, the social processes unfolding in these envirnoments are fundamentally affected by the computational architectures in which they occur. Situational analytics offers a way to address this problematic by making a heterogeneously composed situation – involving social, technical and media elements – the unit of computational analysis. To conclude, I show how situational analytics can be applied in a case study of YouTube videos featuring intelligent vehicles and discuss how situational analysis itself needs to be elaborated if we are to come to terms with computational transformations of the situational fabric of social life. (shrink)

The explosion of data grows at a rate of roughly five trillion bits a second, giving rise to greater urgency in conceptualizing the infosphere and understanding its implications for knowledge and public policy. Philosophers of technology and information technologists alike who wrestle with ontological and epistemological questions of digital information tend to emphasize, as Floridi does, information as our new ecosystem and human beings as interconnected informational organisms, inforgs at home in ambient intelligence. But the linguistic and conceptual representations (...) of Big Data—the massive volume of both structured and unstructured data—and the real world practice of data-mining for patterns and meaningful interpretation of evidence reveal tension and ambiguity in the bold promise of data analytics. This paper explores the tacit epistemology of the rhetoric and representation of Big Data and suggests a richer account of its ambiguities and the paradox of its real world materiality. We argue that Big Data should be recognized as manifesting multiple and conflicting trajectories that reflect human intentionality and particular patterns of power and authority. Such patterns require attentive exploration and moral appraisal if we are to resist simplistic informationist ontologies of Big Data, and the subtle forms of control in the political ecology of Big Data that undermine its promise as transformational knowledge. (shrink)

The experience of the use of applied containerized biomedical software tools in cloud environment is summarized. The reproducibility of scientific computing in relation with modern technologies of scientific calculations is discussed. The main approaches to biomedical data preprocessing and integration in the framework of the intelligent analytical system are described. At the conditions of pandemic, the success of health care system depends significantly on the regular implementation of effective research tools and population monitoring. The earlier the risks of (...) disease can be identified, the more effective process of preventive measures or treatments can be. This publication is about the creation of a prototype for such a tool within the project «Development of methods, algorithms and intelligent analytical system for processing and analysis of heterogeneous clinical and biomedical data to improve the diagnosis of complex diseases», implementted by the V.M. Glushkov Institute of Cybernetics, National Academy of Sciences of Ukraine, together with the United Institute of Informatics Problems, National Academy of Sciences of Belarus. The insurers, entering the market, can insure mostly low risks by facilitating more frequent changes of insurers by consumers and mixing the overall health insurance market. Socio-demographic variables can be risk adjusters. Since age and gender have a relatively small explanatory power, other socio-demographic variables were studied – marital status, retirement status, disability status, educational level, income level. Because insurers have an interest in beneficial diagnoses for their policyholders, they are also interested in the ability to interpret relevant information – upcoding: insurers can encourage their policyholders to consult with doctors more often to select as many diagnoses as possible. Many countries and health care systems use diagnostic information to determine the reimbursement to a service provider, revealing the necessary data. For processing and analysis of these data, software implementations of construction for classifiers, allocation of informative features, processing of heterogeneous medical and biological variables for carrying out scientific research in the field of clinical medicine are developed. The experience of the use of applied containerized biomedical software tools in cloud environment is summarized. The reproducibility of scientific computing in relation with modern technologies of scientific calculations is discussed. Particularly, attention is paid to containerization of biomedical applications, this permits to get reproducibility of the conditions in which the calculations took place, technologies of software pipelining of calculations, that allows to organize flow calculations, and technologies for parameterization of software environment, that allows to reproduce, if necessary, an identical computing environment. The main approaches to biomedical data preprocessing and integration in the framework of the intelligent analytical system are described. The experience of using the developed linear classifier, gained during its testing on artificial and real data, allows us to conclude about several advantages provided by the containerized form of the created application: it permits to provide access to real data located in cloud environment; it is possible to perform calculations to solve research problems on cloud resources both with the help of developed tools and with the help of cloud services; such a form of research organization makes numerical experiments reproducible, i.e. any other researcher can compare the results of their developments on specific data that have already been studied by others, in order to verify the conclusions and technical feasibility of new results; there exists a universal opportunity to use the developed tools on technical devices of various classes from a personal computer to powerful cluster. (shrink)

Academia is a competitive environment. Early Career Researchers (ECRs) are limited in experience and resources and especially need achievements to secure and expand their careers. To help with these issues, this book offers a new approach for conducting research using the combination of mindsponge innovative thinking and Bayesian analytics. This is not just another analytics book. 1. A new perspective on psychological processes: Mindsponge is a novel approach for examining the human mind’s information processing mechanism. This conceptual framework is used (...) to construct models in studies. The framework is highly flexible and widely applicable for many different types of information processes. The mindsponge approach can help researchers discover interesting ideas or even formulate their very own theories when investigating psychosocial phenomena. This approach brings a fresh wind to the current landscape of social sciences and humanities (SSH). 2. Easy-to-follow analysis protocol: The Bayesian Mindsponge Framework (BMF analytics) is useful in terms of computing and visualizing power but also easy to learn and apply. Contrary to being intimidating, the Bayesian analytics section of this book is presented in a reader-friendly manner with a detailed yet clear step-by-step procedure. Examples are from published BMF articles, allowing readers to immediately practice the method and quickly create their own applications. With educational purposes in mind, the book is very suitable for ECRs who are looking to innovate their research methods. 3. Advocating for low-cost, high-quality research: Doing science can be very costly. Mindsponge innovative thinking and BMF analytics help produce impactful studies using openly available data on online repositories. This is based on the authors’ previous works and experiences. The book presents examples of employing the open R package bayesvl on secondary data from different sources. With less financial constraints, researchers can have more freedom of thought to pursue their curiosity and creativity. ECRs in low- and middle-income countries may find this aspect crucial in their careers. 4. Support and collaboration: The authors share their insights from experiences in the academic publishing system to help readers get through the processes of manuscript writing and peer-reviewing more easily. The authors are also ready to support other researchers with further inquiries and collaboration opportunities at the following website, mindsponge(dot)info. This book is for: a) ECRs whose only abundant resources are their innovation capacity and strength of will; b) Researchers in SSH who want to explore a novel approach to thinking and study conducting; c) Low- and middle-income countries’ researchers looking for a cost-effective research protocol; and, d) Innovative thinkers who want to turn their interesting thoughts into good publications. (shrink)

Recent years have seen incredible advances in our abilities to gather and store data, as well as in the computational power and methods—most prominently in machine learning—to do things with those data. These advances have given rise to the emerging field “data science.” Because of its immense power for providing practically useful information about the world, data science is a field of increasing importance. This paper argues that a core part of what data scientists are (...) doing should be understood as conceptual engineering. At all stages of the data science process, data scientists need to deliberate about, evaluate, and make classificatory choices in a variety of ways, including as part of training and evaluating machine learning models. Viewing these activities as involved in conceptual engineering offers a new way to think about them, one that helps to clarify what is at stake in them, what sorts of considerations are relevant, and how to systematically think about the choices faced. Given the increasing importance of data science, if conceptual engineering is relevant for activities in data science, this also highlights the relevance and impact of conceptual engineering as a method. Furthermore, the paper also suggests that machine learning opens distinctive and novel ways in which data scientists engage in conceptual engineering. (shrink)

The era of Big Data comes with the omnipresent metaphor of the Cloud, a term suggesting an ephemeral and seemingly endless storage space, unhindered by time and place. Similar to the satellite image of the Whole Earth, which was the icon of technological progress in the late 60s, the Cloud as a metaphor breathes the promise of technology, whilst obfuscating the hardware reality of server farms and software infrastructure necessary to enable the proliferation of data. This (...) article presents projects from the fields of humanities, social sciences and the arts that formulate a response to Big Data and its human and automated practices, from data analytics dashboards to critical reflections on smart technologies and objects. (shrink)

Detecting quality in large unstructured datasets requires capacities far beyond the limits of human perception and communicability and, as a result, there is an emerging trend towards increasingly complex analytic solutions in data science to cope with this problem. This new trend towards analytic complexity represents a severe challenge for the principle of parsimony (Occam’s razor) in science. This review article combines insight from various domains such as physics, computational science, data engineering, and cognitive science to review the (...) specific properties of big data. Problems for detecting data quality without losing the principle of parsimony are then highlighted on the basis of specific examples. Computational building block approaches for data clustering can help to deal with large unstructured datasets in minimized computation time, and meaning can be extracted rapidly from large sets of unstructured image or video data parsimoniously through relatively simple unsupervised machine learning algorithms. Why we still massively lack in expertise for exploiting big data wisely to extract relevant information for specific tasks, recognize patterns and generate new information, or simply store and further process large amounts of sensor data is then reviewed, and examples illustrating why we need subjective views and pragmatic methods to analyze big data contents are brought forward. The review concludes on how cultural differences between East and West are likely to affect the course of big data analytics, and the development of increasingly autonomous artificial intelligence (AI) aimed at coping with the big data deluge in the near future. Keywords: big data; non-dimensionality; applied data science; paradigm shift; artificial intelligence; principle of parsimony (Occam’s razor) . (shrink)

The Harold Pinter Archive at the British Library contains correspondence dating from 1977 to 2008, including his e-mail archive. In total, the correspondence strand of the archive contains c.20,000 paper letters and c.3500 e-mails. This project used data analytics (in Python) and network visualisation (in Gephi) to interrogate the ways in which digital and analogue correspondence function together within Pinter’s literary archive. The attendant paper reflects upon what this analysis might mean for archivists, curators and researchers working with hybrid (...) correspondence collections in the context of Digital Humanities and Artificial Intelligence based research methodologies. The paper includes an analysis of what an email is both materially and functionally—what are its constituent parts and in what ways is it like and unlike a physical letter, as well as a discussion of how we can leverage the highly structured nature of e-mail data to our advantage using computational techniques, and the limitations of these approaches to socalled “dark archives”. Network visualisations are used to represent correspondence visually in order to shed light on the activities it represents; particularly literary collaboration and administration. The paper concludes with an attempt to draw conclusions from these analyses and to think about their implications for repositories and researchers collecting and using hybrid correspondence collections. The Python code produced for the project is collection agnostic and available under a Creative Commons Licence, meaning that any collecting repository holding email archives in MBOX format can use it to extract GDPR compliant metadata from their collections and visualise them in Gephi. (shrink)

Data science, and the related field of big data, is an emerging discipline involving the analysis of data to solve problems and develop insights. This rapidly growing domain promises many benefits to both consumers and businesses. However, the use of big data analytics can also introduce many ethical concerns, stemming from, for example, the possible loss of privacy or the harming of a sub-category of the population via a classification algorithm. To help address these potential ethical (...) challenges, this paper maps and describes the main ethical themes that were identified via systematic literature review. It then identifies a possible structure to integrate these themes within a data science project, thus helping to provide some structure in the on-going debate with respect to the possible ethical situations that can arise when using data science analytics. (shrink)

Despite its elusiveness as a concept, ‘artificial intelligence’ (AI) is becoming part of everyday life, and a range of empirical and methodological approaches to social studies of AI now span many disciplines. This article reviews the scope of ethnomethodological and conversation analytic (EM/CA) approaches that treat AI as a phenomenon emerging in and through the situated organization of social interaction. Although this approach has been very influential in the field of computational technology since the 1980s, AI has only recently emerged (...) as such a pervasive part of daily life to warrant a sustained empirical focus in EM/CA. Reviewing over 50 peer-reviewed publications, we find that the studies focus on various social and group activities such as task-oriented situations, semi-experimental setups, play, and everyday interactions. They also involve a range of participant categories including children, older participants, and people with disabilities. Most of the reviewed studies apply CA’s conceptual apparatus, its approach to data analysis, and core topics such as turn-taking and repair. We find that across this corpus, studies center on three key themes: openings and closing the interaction, miscommunication, and non-verbal aspects of interaction. In the discussion, we reflect on EM studies that differ from those in our corpus by focusing on praxeological respecifications of AI-related phenomena. Concurrently, we offer a critical reflection on the work of literature reviewing, and explore the tortuous relationship between EM and CA in the area of research on AI. (shrink)

Computational social science, as an emerging interdisciplinary discipline, is a field ushered in by long-term development of traditional social science. It is committed to supplying data thinking, resources, and analytics to study human social behavior and social operation laws to accurately grasp and judge the developing path of the discipline, which is of great significance to promote the innovation and development of social sciences. This study is to conduct a systematic quantitative analysis from a bibliometric perspective, aiming to provide (...) a reference for scholars to explore the paths and changing rules in the field. We use the relevant literature in Web of Science as the dataset. After eliminating journal calls and irrelevant literature, R language and SciMAT tools are used to visualize and analyze the number of articles, keyword clustering, keyword cooccurrence network, and theme evolution, so as to summarize and sort out the paths of computational social science research. The study found that the annual volume of publications has been gradually increasing and will probably remain active in the next few years with high productivity. Subject themes in different periods are diversified, and the evolutionary relationship is found complex as well. Besides, as a cross discipline, scientific knowledge from different fields cross collides and couples with each other in the big data environment, changing the traditional concept of computational social science and forming a new development path. Recently, the emergence of “big data+” has promoted the rise of new subject areas, making the development of new disciplines a reality. (shrink)

This project uses machine learning and computer vision techniques and a novel interactive visualization tool to provide street-level characterization of urban spaces such as safety and maintenance in urban neighborhoods. This is achieved by collecting and annotating street-view images, extracting objective metrics through computer vision techniques, and using crowdsourcing to statistically model the perception of subjective metrics such as safety and maintenance. For modeling human perception and scaling it up with a predictive algorithm, we evaluate perception predictions across two points (...) in time separated by economic changes in the urban core of Raleigh, North Carolina, in the aftermath of the 2008 Great Recession. We hypothesize specific socioeconomic processes can be substantially reflected in the built environment of cities and, thus, render themselves visible at the street level. This paper describes the process of incorporating subjective visual ratings across two datasets of temporally separated street-view images, an algorithm, and a visualization tool. This work serves as a case study for utilizing AI and visualization techniques in a richer characterization of urban spaces that includes both objective metrics such as income (that operates at a broader scale) and subjective metrics such as perception of individuals (that operates at a narrower scale at specific locations). We outline an interdisciplinary methodology to test this hypothesis in streetscape data from Raleigh, NC, from 2008 to 2020. We describe the results of training algorithms that utilized image features with crowdsourced human perception ratings. We provide a comparison of the results with income data. The analysis and interpretation of this comparison provide insight into the challenges and opportunities for using AI technology in characterizing changes in urban environments. One challenge is the ability of human domain experts to interpret the output of algorithms through manipulation and to integrate these results into their workflow. This is addressed with a novel interface designed for interactive analysis and visualization. We conclude with a discussion of some of the benefits and limitations of integrating AI models in the human expert’s decision-making process in the presence of both subjective and objective metrics. (shrink)

Science and technology studies and the emerging field of data science share surprising elective affinities. At the growing intersections of these fields, there will be many opportunities and not a few thorny difficulties for STS scholars. First, I discuss how both fields frame the rollout of data science as a simultaneously social and technical endeavor, even if in distinct ways and for diverging purposes. Second, I discuss the logic of domains in contemporary computer, information, and data science (...) circles. While STS is often agnostic about the borders between the sciences or with industry and state—occasionally taking those boundaries as an object of study—data science takes those boundaries as its target to overcome. These two elective affinities present analytic and practical challenges for STS but also opportunities for engagement. Overall, in addition to these typifications, I urge STS scholars to strategically position themselves to investigate and contribute to the breadth of transformations that seek to touch virtually every science and newly bind spheres of academy, industry, and state. (shrink)

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)

This paper explores the gray area of questionable research practices (QRPs) between responsible conduct of research and severe research misconduct in the form of fabrication, falsification, and plagiarism (Steneck in SEE 12(1): 53–57, 2006). Up until now, we have had very little knowledge of disciplinary similarities and differences in QRPs. The paper is the first systematic account of variances and similarities. It reports on the findings of a comprehensive study comprising 22 focus groups on practices and perceptions of QRPs across (...) main areas of research. The paper supports the relevance of the idea of epistemic cultures (Knorr Cetina in: Epistemic cultures: how the sciences make knowledge, Harvard University Press, Cambridge, 1999), also when it comes to QRPs. It shows which QRPs researchers from different areas of research (humanities, social sciences, medical sciences, natural sciences, and technical sciences) report as the most severe and prevalent within their fields. Furthermore, it shows where in the research process these self-reported QRPs can be found. This is done by using a five-phase analytical model of the research process (idea generation, research design, data collection, data analysis, scientific publication and reporting). The paper shows that QRPs are closely connected to the distinct research practices within the different areas of research. Many QRPs can therefore only be found within one area of research, and QRPs that cut across main areas often cover relatively different practices. In a few cases, QRPs in one area are considered good research practice in another. (shrink)

When making a decision, humans consider two types of information: information they have acquired through their prior experience of the world, and further information they gather to support the decision in question. Here, we present evidence that data from search engines such as Google can help us model both sources of information. We show that statistics from search engines on the frequency of content on the Internet can help us estimate the statistical structure of prior experience; and, specifically, we (...) outline how such statistics can inform psychological theories concerning the valuation of human lives, or choices involving delayed outcomes. Turning to information gathering, we show that search query data might help measure human information gathering, and it may predict subsequent decisions. Such data enable us to compare information gathered across nations, where analyses suggest, for example, a greater focus on the future in countries with a higher per capita GDP. We conclude that search engine data constitute a valuable new resource for cognitive scientists, offering a fascinating new tool for understanding the human decision-making process. (shrink)

The nature of creative engagement with computers and software presents a number of challenges to 4E cognition and requires the development of analytical frameworks that can encompass cognitive processes as they extend across material and informational realms. Here I argue that an enactive view of mind allows for better understanding of digital practice by advancing a dynamic, transactional, and affective framework for the analysis of computational design. This enactive framework is in part developed through the Material Engagement Theory put forward (...) by Lambros Malafouris, in part from the phenomenologically inspired philosophy of Bernard Stiegler. Both advance temporality, technics and technique as key to understanding human creative imagination and their work can support each other in different ways; Stiegler allows for a theorisation of digital tools largely missing from the cognitive archaeology of Malafouris, whilst Malafouris provides a cognitive theory to further develop key ideas in Stiegler’s philosophy. Bringing their work together through Gilbert Simondon’s theory of individuation, I develop the concept of enactive individuation and apply this to the analysis of a case of robotic design and fabrication from my fieldwork with digital architects and engineers. This case allows for further exploration of how enactivism might productively be extended into the digital realm by underscoring the explorative engagement at heart of even highly systematic work with computers and software. (shrink)

Big data biology—bioinformatics, computational biology, systems biology (including ‘omics’), and synthetic biology—raises a number of issues for the philosophy of science. This article deals with several such: Is data-intensive biology a new kind of science, presumably post-reductionistic? To what extent is big data biology data-driven? Can data ‘speak for themselves?’ I discuss these issues by way of a reflection on Carl Woese’s worry that “a society that permits biology to become an engineering discipline, that allows (...) that science to slip into the role of changing the living world without trying to understand it, is a danger to itself.” And I argue that scientific perspectivism, a philosophical stance represented prominently by Giere, Van Fraassen, and Wimsatt, according to which science cannot as a matter of principle transcend our human perspective, provides the best resources currently at our disposal to tackle many of the philosophical issues implied in the modeling of complex, multilevel/multiscale phenomena. (shrink)

With the deployment of information and communication technologies and the needs of data and information sharing within cities, smart city aims to provide value-added services to improve citizens’ quality of life. But, currently city planners/developers are faced with inadequate contextual information on the dimensions of smart city required to achieve a sustainable society. Therefore, in achieving sustainable society, there is need for stakeholders to make strategic decisions on how to implement smart city initiatives. Besides, it is required to specify (...) the smart city dimensions to be adopted in making cities smarter for sustainability attainment. But, only a few methods such as big data, internet of things, cloud computing, etc. have been employed to support smart city attainment. Thus, this study integrates case-based reasoning as an artificial intelligence technique to develop a recommender system towards promoting smart city planning. CBR provides suggestions on smart city dimensions to be adopted by city planners/decision-makers in making cities smarter and sustainable. Accordingly, survey data were collected from 115 respondents to evaluate the applicability of the implemented CBR recommender system in relation to how the system provides best practice recommendations and retaining of smart city initiatives. Results from descriptive and exploratory factor analyses suggest that the developed system is applicable in supporting smart city adoption. Besides, findings from this study are expected to provide valuable insights for practitioners to develop more practical strategies and for researchers to better understand smart city dimensions. (shrink)

Computer models and simulations have become, since the 1960s, an essential instrument for scientific inquiry and political decision making in several fields, from climate to life and social sciences. Philosophical reflection has mainly focused on the ontological status of the computational modeling, on its epistemological validity and on the research practices it entails. But in computational sciences, the work on models and simulations are only two steps of a longer and richer process where operations on data are as important (...) as, and even more time and energy-consuming than modeling itself. Drawing on two study cases—computational embryology and computational epidemiology—this article contributes to filling the gap by focusing on the operations of producing and re-using data in computational sciences. The different phases of the scientific and artisanal work of modelers include data collection, aggregation, homogenization, assemblage, analysis and visualization. The article deconstructs the ideas that data are self-evident informational aggregates and that data-driven approaches are exempted from theoretical work. More importantly, the paper stresses the fact that data are constructed and theory laden not only in their fabrication, but also in their reusing. (shrink)

Visual stylometry is a new interdisciplinary research field that sits at the junction of digital humanities, empirical aesthetics, and computer science. Research in this field employs image analysis algorithms to study key aspects of artistic style. The nature of artistic style is the subject of ongoing debate within art history and philosophy of art. Computational and statistical methods in visual stylometry allow researchers to quantify and compare aspects of artistic style over the course of the career of an individual artist, (...) among artists who share in a common artistic style, and across different schools of art. The results of these studies provide clues to the image features that enable viewers to categorize works as belonging to different artistic styles. -/- We have developed a system that can act as an open access laboratory for visual stylometry. This approach is powerful enough to support advanced academic research in computer science, cognitive science, art history, and the philosophy of art while at the same time providing a user friendly interface accessible to students and researchers with little or no computer science background. Our framework is designed around the idea of scientific workflows. Scientific workflows allow users to build executable analyses in the same way they would draw a flowchart, by dragging objects representing data sets and image analysis procedures onto the work-space and drawing links between them. These workflows and data sets can be exported using open standards as web objects that can then be shared or modified by other users. -/- Our platform, Workflows for the Analysis of Images in Visual Stylometry (WAIVS), has the potential to promote computational literacy and data analytic skills among humanities students, to introduce science students to research in art and the humanities, to explore the nature of artistic style and its role in our understanding of artwork, and to help researchers in cognitive science understand how viewers perceptually categorize, recognize, and otherwise engage with artworks. (shrink)

Embedding ethics modules within computer science courses has become a popular response to the growing recognition that CS programs need to better equip their students to navigate the ethical dimensions of computing technologies like AI, machine learning, and big data analytics. However, the popularity of this approach has outpaced the evidence of its positive outcomes. To help close that gap, this empirical study reports positive results from Northeastern’s program that embeds values analysis modules into CS courses. The resulting (...) class='Hi'>data suggest that such modules have a positive effect on students’ moral attitudes and that students leave the modules believing they are more prepared to navigate the ethical dimensions they’ll likely face in their eventual careers. Importantly, these gains were accomplished at an institution without a philosophy doctoral program, suggesting this strategy can be effectively employed by a wider range of institutions than many have thought. (shrink)

This article presents our experience as a multidisciplinary team systematizing and analyzing the transcripts from a large-scale (1.775 conversations) series of conversations about Chile’s future. This project called “Tenemos Que Hablar de Chile” [We have to talk about Chile] gathered more than 8000 people from all municipalities, achieving gender, age, and educational parity. In this sense, this article takes an experiential approach to describe how certain interdisciplinary methodological decisions were made. We sought to apply analytical variables derived from social science (...) theories and operationalize them through modern linguistics to guide a more theoretically informed natural language processing. The analysis was divided into three stages: (1) a descriptive analysis adapting descriptions of computational grounded theory, (2) a futurization analysis operationalizing concepts from futures studies, and (3) an argumentative analysis operationalizing concepts from argumentation theory. Overall, our methodological experimentation shed light on potential learnings for integrating a multidisciplinary perspective on NLP analysis with sensitive social content. Firstly, we developed a strategy for translation of knowledge based on the construction of what we called "analytical categories” in which a normative expectation or descriptive dimension was identified in the body of literature, operationalized through linguistics, and programmed in Python or R. Ultimately, we seek to reflect on the importance interdisciplinarity not only as means to find new analysis ideas but rather, to incorporate the critical, political and epistemological points of view to understand analysis as complex socio-technical processes. (shrink)

GIS for Science presents a collection of real-world stories about modern science and a cadre of scientists who use mapping and spatial analytics to expand their understanding of the world. The accounts in this book are written for a broad audience including professional scientists, the swelling ranks of citizen scientists, and people generally interested in science and geography. Scientific data are brought to life with GIS technology to study a range of issues relevant to the functioning of planet Earth (...) in a natural sense as well as the impacts of human activity. In a race against the clock, the scientists profiled in this volume are using remote sensing, web maps within a geospatial cloud, Esri StoryMaps, and spatial analysis to document and solve an array of issues with a geographic dimension, ranging from climate change, natural disasters, and loss of biodiversity, to homelessness, loss of green infrastructure, and resource shortages. These stories present geospatial ideas and inspiration that readers can apply across many disciplines, making this volume relevant to a diverse scientific audience. See how scientists working on the world's most pressing problems apply geographic information systems--GIS. -- "Mike Goodchild". (shrink)

Engineering sketches and drawings are the building blocks of technological design and production. These visual representations act as the means for organizing the design to production process, hence serving as a "social glue" both between individuals and between groups. The author discusses two main capacities such visual representations serve in facilitating distributed cognition in team design work As conscription devices, they enlist and organize group participation. As boundary objects, they facilitate the reading of alternative meanings by various groups involved in (...) the design process. The introduction of computer-aided design into this visual culture of engineering restructures relationships between workers in ways that can hamper the flexibility necessary for these crucial capacities to take place. The data are drawn from a study of the daily practices of engineers engaged in redesigning a turbine engine package. The method is participant observation. (shrink)

Scientists have rules pertaining to data fabrication and falsification that are enforced with significant punishments, such as loss of funding, termination of employment, or imprisonment. These rules pertain to data that describe observable and unobservable entities. In this commentary I argue that scientists would not adopt rules that impose harsh penalties on researchers for data fabrication or falsification unless they believed that an aim of scientific research is to develop true theories and hypotheses about entities that exist, (...) including unobservable ones. This argument presents a challenge for constructive empiricists, such as van Fraassen. Constructive empiricists need to be able to explain why rules pertaining to data fabrication and falsification do not threaten their philosophy of science. (shrink)

Big social data have enabled new opportunities for evaluating the applicability of social science theories that were formulated decades ago and were often based on small- to medium-sized samples. Big Data coupled with powerful computing has the potential to replace the statistical practice of sampling and estimating effects by measuring phenomena based on full populations. Preparing these data for analysis and conducting analytics involves a plethora of decisions, some of which are already embedded in previously collected (...) class='Hi'>data and built tools. These decisions refer to the recording, indexing and representation of data and the settings for analysis methods. While these choices can have tremendous impact on research outcomes, they are not often obvious, not considered or not being made explicit. Consequently, our awareness and understanding of the impact of these decisions on analysis results and derived implications are highly underdeveloped. This might be attributable to occasional high levels of over-confidence in computational solutions as well as the possible yet questionable assumption that Big Data can wash out minor data quality issues, among other reasons. This article provides examples for how to address this issue. It argues that checking, ensuring and validating the quality of big social data and related auxiliary material is a key ingredient for empowering users to gain reliable insights from their work. Scrutinizing data for accuracy issues, systematically fixing them and diligently documenting these processes can have another positive side effect: Closely interacting with the data, thereby forcing ourselves to understand their idiosyncrasies and patterns, can help us to move from being able to precisely model and formally describe effects in society to also understand and explain them. (shrink)

We examine the philosophical disputes among computer scientists concerning methodological, ontological, and epistemological questions: Is computer science a branch of mathematics, an engineering discipline, or a natural science? Should knowledge about the behaviour of programs proceed deductively or empirically? Are computer programs on a par with mathematical objects, with mere data, or with mental processes? We conclude that distinct positions taken in regard to these questions emanate from distinct sets of received beliefs or paradigms within the discipline: – The (...) rationalist paradigm, which was common among theoretical computer scientists, defines computer science as a branch of mathematics, treats programs on a par with mathematical objects, and seeks certain, a priori knowledge about their ‘correctness’ by means of deductive reasoning. – The technocratic paradigm, promulgated mainly by software engineers and has come to dominate much of the discipline, defines computer science as an engineering discipline, treats programs as mere data, and seeks probable, a posteriori knowledge about their reliability empirically using testing suites. – The scientific paradigm, prevalent in the branches of artificial intelligence, defines computer science as a natural (empirical) science, takes programs to be entities on a par with mental processes, and seeks a priori and a posteriori knowledge about them by combining formal deduction and scientific experimentation. We demonstrate evidence corroborating the tenets of the scientific paradigm, in particular the claim that program-processes are on a par with mental processes. We conclude with a discussion in the influence that the technocratic paradigm has been having over computer science. (shrink)

Over the last decade, humans have produced each year as much data as were produced throughout the entire history of humankind. These data, in quantities that exceed current analytical capabilities, have been described as “the new oil,” an incomparable source of value. This is true for healthcare, as well. Conducting analyses of large, diverse, medical datasets promises the detection of previously unnoticed clinical correlations and new diagnostic or even therapeutic possibilities. However, using Big Data poses several problems, (...) especially in terms of representing the uniqueness of each patient and expressing the differences between individuals, primarily gender and sex differences. The first two sections of the paper provide a definition of “Big Data” and illustrate the uses of Big Data in medicine. Subsequently, the paper explores the struggle to represent exhaustively the uniqueness of the patient through Big Data is highlighted prior to a deeper investigation of the digital representation of gender in personalized medicine. The final part of the paper put forward a series of recommendations for better approaching the complexity of gender in medical and clinical research involving Big Data for the creation or enhancement of personalized medicine services. (shrink)

It is conventionally understood that computers play a rather limited role in theoretical mathematics. While computation is indispensable in applied mathematics and the theory of computing and algorithms is rich and thriving, one does not, even today, expect to find computers in theoretical mathematics settings beyond the theory of computing. Where computers are used, by those studying combinatorics , algebra, number theory, or dynamical systems, the computer most often assumes the role of an automated and speedy theoretician, performing manipulations and (...) checking cases in a way assumed to be possible for human theoreticians, if only they had the time, the memory, and the precision. Automated proofs have become standard tools in mathematical logic, and it is often expected that proofs be published in a computer-checkable format. It is not surprising, then, that most philosophical work on computers in theoretical mathematics has been on computers' roles as supplementary mathematicians. Donald MacKenzie's 2001 book Mechanizing Proof demonstrates the rich potential for social and historical studies to complement the substantial analytic debate in this area of philosophy. But what of computers in theoretical mathematics behaving as computers, and not as mere mechanized mathematicians? Very little role is commonly assumed for computers working as supplements to mathematicians, rather than as supplementary mathematicians themselves. Accordingly, very little philosophy has attempted to grapple with theoretical mathematics in which computers play an essential but essentially non-theoretical role. My presentation will draw on work I conducted as a researcher in harmonic analysis on fractals at Cornell University. I will analyze the explicit and implicit conceptual apparatus employed in my and my fellow researchers' use of computers in the theoretical study of second order differential equations, such as those for sound and heat flow, on various fractal analogues of the Sierpinski gasket. Such gaskets are easy to visualize in very crude approximation in a low number of dimensions. As one increases the complexity of the gasket or the refinement of one's analysis, visualization and precise computation become impossible, and soon computers are unable to produce even approximate data to model differential equations in these situations. We thus had to carefully choose analytic approaches and methods to make our theoretical mathematics amenable to computer simulation. In my case, studying the transformation of the gaskets as they are expanded into increasingly high dimensions, computer simulation eventually required that the problem be reimagined entirely in terms of interlinked systems of parameters. This computer-approximation-driven theoretical orientation shaped my mathematical intuitions toward the problem and guided my fellow researchers and me in both theoretical and computational directions. We discovered both that computer approximation could be incredibly powerful as an aid to intuition, and that it can be incredibly difficult to transfer computer-oriented mathematics back into the purely theoretical standards of our area of specialty. I will address the philosophical implications of computer-driven theoretical mathematics, asking how computer experiments can shape both the content and standards of theoretical sciences. (shrink)

This textbook presents an overview of the critically important ethical and legal issues that arise in the computing field and provides a professional perspective from software engineering. The author gained exposure to these aspects of computing while working as a software engineer at Motorola in Ireland, where he coordinated the patent programme and worked with several software suppliers. Topics and features: Presents a broad overview of ethics and the law Includes key learning topics, summaries, and review questions in each chapter, (...) together with a useful glossary Discusses the professional responsibility of computer professionals Explores ethics in various civilisations and religious traditions Discusses ethical software engineering and ethical outsourcing Considers what is fair and ethical in data science Describes ethical challenges that arise in social media and the AI field Reviews intellectual property including patents, copyright and trademarks This practical and easy-to-follow textbook/reference is ideal for computer science students seeking to understand legal and ethical aspects of computing. The text also serves as a concise self-study primer for software engineers and software managers. (shrink)

Because the label "computing and philosophy" can seem like an ad hoc attempt to tie computing to philosophy, it is important to explain why it is not, what it studies (or does) and how it differs from research in, say, "computing and history," or "computing and biology". The American Association for History and Computing is "dedicated to the reasonable and productive marriage of history and computer technology for teaching, researching and representing history through scholarship and public history" (http://theaahc.org). More pervasive, (...) work in computing and biology enjoys the convenient name of "bioinformatics...the science of using information to understand biology..., a subset of the larger field of computational biology, the application of quantitative analytical techniques in modeling biological systems" (http://oreilly.com/catalog/bioskills/chapter/ ch01.html). The recent venture of the Association for Computing Machinery and the Institute of Electrical and Electronics Engineers to publish the Transactions on Computational Biology and Bioinformatics (TCBB) bears witness to the reach of computing and biology and underscores its objective. TCBB intends to report "archival research results related to the algorithmic, mathematical, statistical, and computational methods that are central in bioinformatics and computational biology; the development and testing of effective computer programs in bioinformatics; the development and optimization of biological databases; and important biological results that are obtained from the use of these methods, programs, and databases" (http://tcbb.acm.org). In the case of "computing and history" and "bioinformatics," each discipline stands in a particular relationship to computers that raises questions unique to itself. But both are devoted to the development of computational tools to aid discovery.. (shrink)

The Handbook of Computational Social Science is a comprehensive reference source for scholars across multiple disciplines. It outlines key debates in the field, showcasing novel statistical modeling and machine learning methods, and draws from specific case studies to demonstrate the opportunities and challenges in CSS approaches. The Handbook is divided into two volumes written by outstanding, internationally renowned scholars in the field. This first volume focuses on the scope of computational social science, ethics, and case studies. It covers a range (...) of key issues, including open science, formal modeling, and the social and behavioral sciences. This volume explores major debates, introduces digital trace data, reviews the changing survey landscape, and presents novel examples of computational social science research on sensing social interaction, social robots, bots, sentiment, manipulation, and extremism in social media. The volume not only makes major contributions to the consolidation of this growing research field, but also encourages growth into new directions. With its broad coverage of perspectives (theoretical, methodological, computational), international scope, and interdisciplinary approach, this important resource is integral reading for advanced undergraduates, postgraduates and researchers engaging with computational methods across the social sciences, as well as those within the scientific and engineering sectors. (shrink)

Pashmina is one of the most luxurious and finest fibres in the world. It is a special kind of wool obtained from Cashmere goats. Counterfeiting Pashmina is becoming a prevalent malpractice because of limited supply, expensive pricing and high demand in western markets. Presently, there is a lack of a low-cost and easily available approach for distinguishing authentic Pashmina apparels from other similar-looking products. Because of technological advances and cost reductions in digital image processing, we have been able to implement (...) a variety of image recognition systems on a practical level. In this study, we propose a deep learning model PashmNet for identification of Pashmina fabric using transfer learning. The technique proposed herein classifies Pashmina and non-Pashmina images accurately with classification accuracy of 97.4%. (shrink)

The aim of this article is to indicate that the ideas of Leonardo Torres y Que­vedo presented in his short Essays on Automatics constitute essential link between early Babbage’s concepts of analytical engine and modern computer science. These ideas include definition of automatics, classification of automata, theoretical basis for robotics, electromechanical engineering, modern concept of chatbot, the importance of algorithm and last but not least floating point arithmetic.

Manufacturing and industry practices are undergoing an unprecedented revolution as a consequence of the convergence of emerging technologies such as artificial intelligence, robotics, cloud computing, virtual and augmented reality, among others. This fourth industrial revolution is similarly changing the practices and capabilities of operators in their industrial environments. This paper introduces and explores the notion of the Operator 4.0 as well as how this novel way of conceptualizing the human operator necessarily implicates human values in the technologies that constitute (...) it. The design approach known as value sensitive design (VSD) is used to explore how these Operator 4.0 technologies can be designed for human values. Expert elicitation surveys were used to determine the values of industry stakeholders and examples of how the VSD methodology can be adopted by engineers in order to design for these values is illustrated. The results provide preliminary adoption strategies that industrial teams can take to Operator 4.0 technology for human values. (shrink)

Though full of promise, Big Data research success is often contingent on access to the newest, most advanced, and often expensive hardware systems and the expertise needed to build and implement such systems. As a result, the accessibility of the growing number of Big Data-capable technology solutions has often been the preserve of business analytics. Pay as you store/process services like Amazon Web Services have opened up possibilities for smaller scale Big Data projects. There is high demand (...) for this type of research in the digital humanities and digital sociology, for example. However, scholars are increasingly finding themselves at a disadvantage as available data sets of interest continue to grow in size and complexity. Without a large amount of funding or the ability to form interdisciplinary partnerships, only a select few find themselves in the position to successfully engage Big Data. This article identifies several notable and popular Big Data technologies typically implemented using large and extremely powerful cloud-based systems and investigates the feasibility and utility of development of Big Data analytics systems implemented using low-cost commodity hardware in basic and easily maintainable configurations for use within academic social research. Through our investigation and experimental case study, we found that not only are solutions like Cloudera’s Hadoop feasible, but that they can also enable robust, deep, and fruitful research outcomes in a variety of use-case scenarios across the disciplines. (shrink)

Networked service organisations are increasingly adopting a ‘smarter networking’ philosophy in their design of more agile and customer-focused supply models. Changing consumer behaviours and the emergence of transformative technologies—industry 4.0, artificial intelligence, big data analytics, the Internet of Things—are driving a series of innovations, in terms of ‘products’ and business models, with major implications for the industrial enterprise, in their design of more ‘digitalised’ supply chains. For B2B systems, emerging ‘product-service’ offerings are requiring greater visibility, alignment and integration across (...) an increasingly complex network of multiple partners and collaborators, in order to deliver a better service and customer ‘experience’. To support the design and operation of these multi-organisational service networks, we outline a concept of operations architecture here, underpinned by the literature and network theory, and demonstrate application using a series of exemplar case studies. Focusing on relational elements and the processes key to network integration within service supply networks, the cases inform a set of operating principles and protocols—applicable to all stakeholders ‘cooperating’, within a ‘shared’ environment. Equally critical is to understand how digital technologies may influence future operating philosophies. This article extends our theoretical understanding of network organisations, from a traditional ‘product’ perspective to that of ‘services’, and presents the case for developing a common, unified approach to designing diverse forms of multi-partner service networks. (shrink)

The objective of this study is to examine the effects of generative artificial intelligence (AI) tools, with a specific focus on ChatGPT, on the analytical proficiencies of policy analysts operating in Indonesia. Considering the increasing intricacies of contemporary governance and the emergence of "wicked problems," this study investigates the potential of AI to facilitate the development of inventive, data-centric public policies. Involving postgraduate students in a quasi-experimental design, this study investigated the efficacy of ChatGPT in assisting in the development (...) of policy solutions pertaining to air pollution in Jakarta. Specialized prompt engineering techniques are utilized in this study to enhance the utility of ChatGPT for policy analysis. The results suggest that, although generative AI tools such as ChatGPT provide systematic frameworks for tackling complex policy issues and may enhance analytical skills, they still require additional refinement to increase their empirical support and practical applicability in the Indonesian policy context. In accordance with incrementalism theory, the study concludes that incorporating AI into policy analysis can facilitate the gradual formulation of evidence-based policies, offering a pragmatic approach to the resolution of complex problems in the public sector. (shrink)

With increasing publication and data production, scientific knowledge presents not simply an achievement but also a challenge. Scientific publications and data are increasingly treated as resources that need to be digitally ‘managed.’ This gives rise to scientific Knowledge Management : second-order scientific work aiming to systematically collect, take care of and mobilise first-hand disciplinary knowledge and data in order to provide new first-order scientific knowledge. We follow the work of Leonelli, Efstathiou and Hislop in our analysis of (...) the use of KM in semantic systems biology. Through an empirical philosophical account of KM-enabled biological research, we argue that KM helps produce new first-order biological knowledge that did not exist before, and which could not have been produced by traditional means. KM work is enabled by conceiving of ‘knowledge’ as an object for computational science: as explicated in the text of biological articles and computable via appropriate data and metadata. However, these founded knowledge concepts enabling computational KM risk focusing on only computationally tractable data as knowledge, underestimating practice-based knowing and its significance in ensuring the validity of ‘manageable’ knowledge as knowledge. (shrink)

As an advanced network calculation mode, cloud computing is becoming more and more popular. However, with the proliferation of large data centers hosting cloud applications, the growth of energy consumption has been explosive. Surveys show that a remarkable part of the large energy consumed in data center results from over-provisioning of the network resource to meet requests during peak demand times. In this paper, we propose a solution to this problem by constructing a dynamic energy-efficient resource (...) management scheme. As a way of saving energy as well as maintaining cloud user’s quality of experience, the scheme presents a multitier cloud architecture by configuring physical machines into two pools: a hot pool and a warm pool. Each PM is configured with a resource search engine that finds an available virtual machine for the request, and a synchronous sleep mechanism is introduced to the warm pool. To analyze the end-to-end performance of the cloud system’s service with the proposed scheme, we establish a hybrid queueing system composed of three stochastic submodels by using a matrix-geometric solution. Accordingly, the average latency of requests and the energy-saving rate of the system are derived. Through numerical results, we show the influence of the synchronous sleep mechanism on the system performance. Moreover, from the perspective of economics, we build a system cost function to study the trade-off between different performance measures. An improved Salp Swarm Algorithm is presented to minimize the system cost and optimize the sleep parameter. (shrink)

Privacy, Due process and the Computational Turn: The Philosophy of Law Meets the Philosophy of Technology engages with the rapidly developing computational aspects of our world including data mining, behavioural advertising, iGovernment, profiling for intelligence, customer relationship management, smart search engines, personalized news feeds, and so on in order to consider their implications for the assumptions on which our legal framework has been built. The contributions to this volume focus on the issue of privacy, which is often equated with (...) data privacy and data security, location privacy, anonymity, pseudonymity, unobservability, and unlinkability. Here, however, the extent to which predictive and other types of data analytics operate in ways that may or may not violate privacy is rigorously taken up, both technologically and legally, in order to open up new possibilities for considering, and contesting, how we are increasingly being correlated and categorizedin relationship with due process – the right to contest how the profiling systems are categorizing and deciding about us. (shrink)

Integrity is a critical determinant of the effectiveness of research organizations in terms of producing high quality research and educating the new generation of scientists. A number of responsible conduct of research (RCR) training programs have been developed to address this growing organizational concern. However, in spite of a significant body of research in ethics training, it is still unknown which approach has the highest potential to enhance researchers’ integrity. One of the approaches showing some promise in improving researchers’ integrity (...) has focused on the development of ethical decision-making skills. The current effort proposes a novel curriculum that focuses on broad metacognitive reasoning strategies researchers use when making sense of day-to-day social and professional practices that have ethical implications for the physical sciences and engineering. This sensemaking training has been implemented in a professional sample of scientists conducting research in electrical engineering, atmospheric and computer sciences at a large multi-cultural, multi-disciplinary, and multi-university research center. A pre-post design was used to assess training effectiveness using scenario-based ethical decision-making measures. The training resulted in enhanced ethical decision-making of researchers in relation to four ethical conduct areas, namely data management, study conduct, professional practices, and business practices. In addition, sensemaking training led to researchers’ preference for decisions involving the application of the broad metacognitive reasoning strategies. Individual trainee and training characteristics were used to explain the study findings. Broad implications of the findings for ethics training development, implementation, and evaluation in the sciences are discussed. (shrink)

In 2023, online search engine provider Microsoft integrated a language model that provides direct answers to search queries into its search engine Bing. Shortly afterwards, Google also introduced a similar feature to its search engine with the launch of Google Gemini. This introduction of direct answers to search queries signals an important and significant change in online search. This article explores the implications of this new search paradigm. Drawing on Donna Haraway’s theory of _Situated Knowledges_ and Rainer Mühlhoff’s concept (...) of _Sealed Surfaces_, I introduce the term _Sealed Knowledges_ to draw attention to the increasingly difficult access to the plurality of potential answers to search queries through the output of a singular, authoritative, and plausible text paragraph. I argue that the integration of language models for the provision of direct answers into search engines is based on a de-situated and disembodied understanding of knowledge and affects the subjectivities of its users. At the same time, the _sealing of knowledges_ can lead to an increasing spread of misinformation and may make marginalized knowledge increasingly difficult to find. The paper concludes with an outlook on how to resist the increasing _sealing of knowledges_ in online search. (shrink)