This chapter addresses the vital importance of data in AI research, focusing on data collection methods, integrity, and the ethical considerations involved, especially concerning privacy and bias in AI algorithms. It intends to heighten awareness of the ethical implications of data usage in AI, with strategies for maintaining data integrity and reducing bias. A comprehensive discussion on ethical data practices will highlight their critical role in conducting responsible AI research.

Despite all the attention to Big Data and the claims that it represents a “paradigm shift” in science, we lack understanding about what are the qualities of Big Data that may contribute to this revolutionary impact. In this paper, we look beyond the quantitative aspects of Big Data and examine it from a sociotechnical perspective. We argue that a key factor that distinguishes “Big Data” from “lots of data” lies in changes to the traditional, well-established (...) “control zones” that facilitated clear provenance of scientific data, thereby ensuring data integrity and providing the foundation for credible science. The breakdown of these control zones is a consequence of the manner in which our network technology and culture enable and encourage open, anonymous sharing of information, participation regardless of expertise, and collaboration across geographic, disciplinary, and institutional barriers. We are left with the conundrum—how to reap the benefits of Big Data while re-creating a trust fabric and an accountable chain of responsibility that make credible science possible. (shrink)

The value of any kind of data is greatly enhanced when it exists in a form that allows it to be integrated with other data. One approach to integration is through the annotation of multiple bodies of data using common controlled vocabularies or ‘ontologies’. Unfortunately, the very success of this approach has led to a proliferation of ontologies which itself creates obstacles to integration. The Open Biomedical Ontologies (OBO) consortium has set in train a strategy (...) to overcome this problem. Existing OBO ontologies, including the Gene Ontology, are undergoing a process of coordinated reform and new ontologies being created on the basis of an evolving set of shared principles governing ontology development. The result is an expanding family of ontologies designed to be interoperable, logically well-formed, and to incorporate accurate representations of biological reality. We describe the OBO Foundry initiative, and provide guidelines for those who might wish to become involved. (shrink)

Ontologies, as the term is used in informatics, are structured vocabularies comprised of human- and computer-interpretable terms and relations that represent entities and relationships. Within informatics fields, ontologies play an important role in knowledge and data standardization, representation, integra- tion, sharing and analysis. They have also become a foundation of artificial intelligence (AI) research. In what follows, we outline the Coronavirus Infectious Disease Ontology (CIDO), which covers multiple areas in the domain of coronavirus diseases, including etiology, transmission, epidemiology, pathogenesis, (...) diagnosis, prevention, and treatment. We emphasize CIDO development relevant to COVID-19. (shrink)

We describe a strategy that is being used for the horizontal integration of warfighter intelligence data within the framework of the US Army’s Distributed Common Ground System Standard Cloud (DSC) initiative. The strategy rests on the development of a set of ontologies that are being incrementally applied to bring about what we call the ‘semantic enhancement’ of data models used within each intelligence discipline. We show how the strategy can help to overcome familiar tendencies to stovepiping of (...) intelligence data, and describe how it can be applied in an agile fashion to new data resources in ways that address immediate needs of intelligence analysts. (shrink)

This paper discusses what it means and what it takes to integrate data in order to acquire new knowledge about biological entities and processes. Maureen O’Malley and Orkun Soyer have pointed to the scientific work involved in data integration as important and distinct from the work required by other forms of integration, such as methodological and explanatory integration, which have been more successful in captivating the attention of philosophers of science. Here I explore what (...) class='Hi'>data integration involves in more detail and with a focus on the role of data-sharing tools, like online databases, in facilitating this process; and I point to the philosophical implications of focusing on data as a unit of analysis. I then analyse three cases of data integration in the field of plant science, each of which highlights a different mode of integration: inter-level integration, which involves data documenting different features of the same species, aims to acquire an interdisciplinary understanding of organisms as complex wholes and is exemplified by research on Arabidopsis thaliana; cross-species integration, which involves data acquired on different species, aims to understand plant biology in all its different manifestations and is exemplified by research on Miscanthus giganteus; and translational integration, which involves data acquired from sources within as well as outside academia, aims at the provision of interventions to improve human health and is exemplified by research on Phytophtora ramorum. Recognising the differences between these efforts sheds light on the dynamics and diverse outcomes of data dissemination and integrative research; and the relations between the social and institutional roles of science, the development of data-sharing infrastructures and the production of scientific knowledge. (shrink)

The case studies in this book illuminate how arts and humanities tropes can aid in contextualizing Digital Arts and Humanities, Neogeographic and Social Media activity and data through the creation interpretive schemas to study interactions between visualizations, language, human behaviour, time and place.

Volume 24, Issue 10, October 2024, Page 101-103.

Volume 24, Issue 10, October 2024, Page 101-103.

According to the theory of contextual integrity (CI), privacy norms prescribe information flows with reference to five parameters — sender, recipient, subject, information type, and transmission principle. Because privacy is grasped contextually (e.g., health, education, civic life, etc.), the values of these parameters range over contextually meaningful ontologies — of information types (or topics) and actors (subjects, senders, and recipients), in contextually defined capacities. As an alternative to predominant approaches to privacy, which were ineffective against novel information practices enabled by (...) IT, CI was able both to pinpoint sources of disruption and provide grounds for either accepting or rejecting them. Mounting challenges from a burgeoning array of networked, sensor-enabled devices (IoT) and data-ravenous machine learning systems, similar in form though magnified in scope, call for renewed attention to theory. This Article introduces the metaphor of a data (food) chain to capture the nature of these challenges. With motion up the chain, where data of higher order is inferred from lower-order data, the crucial question is whether privacy norms governing lower-order data are sufficient for the inferred higher-order data. While CI has a response to this question, a greater challenge comes from data primitives, such as digital impulses of mouse clicks, motion detectors, and bare GPS coordinates, because they appear to have no meaning. Absent a semantics, they escape CI’s privacy norms entirely. (shrink)

In this paper, we analyse the relation between the use of environmental data in contemporary health sciences and related conceptualisations and operationalisations of the notion of environment. We consider three case studies that exemplify a different selection of environmental data and mode of data integration in data-intensive epidemiology. We argue that the diversification of data sources, their increase in scale and scope, and the application of novel analytic tools have brought about three significant conceptual (...) shifts. First, we discuss the EXPOsOMICS project, an attempt to integrate genomic and environmental data which suggests a reframing of the boundaries between external and internal environments. Second, we explore the MEDMI platform, whose efforts to combine health, environmental and climate data instantiate a reframing and expansion of environmental exposure. Third, we illustrate how extracting epidemiological insights from extensive social data collected by the CIDACS institute yields innovative attributions of causal power to environmental factors. Identifying these shifts highlights the benefits and opportunities of new environmental data, as well as the challenges that such tools bring to understanding and fostering health. It also emphasises the constraints that data selection and accessibility pose to scientific imagination, including how researchers frame key concepts in health-related research. (shrink)

We describe a strategy for integration of data that is based on the idea of semantic enhancement. The strategy promises a number of benefits: it can be applied incrementally; it creates minimal barriers to the incorporation of new data into the semantically enhanced system; it preserves the existing data (including any existing data-semantics) in their original form (thus all provenance information is retained, and no heavy preprocessing is required); and it embraces the full spectrum of (...) data sources, types, models, and modalities (including text, images, audio, and signals). The result of applying this strategy to a given body of data is an evolving Dataspace that allows the application of a variety of integration and analytic processes to diverse data contents. We conceive semantic enhancement (SE) as a lightweight and flexible process that leverages the richness of the structured contents of the Dataspace without adding storage and processing burdens to what, in the intelligence domain, will be an already storage- and processing-heavy starting point. SE works not by changing the data to which it is applied, but rather by adding an extra semantic layer to this data. We sketch how the semantic enhancement approach can be applied consistently and in cumulative fashion to new data and data-models that enter the Dataspace. (shrink)

This fieldnotes article documents the design and implementation of a Philosophy for Children (P4C) lesson aimed at introducing children aged 8–12 to the ethical dimensions of Artificial Intelligence (AI), with a particular focus on data privacy. Drawing on the Community of Philosophical Inquiry (CoPI) methodology— specifically the “Plain Vanilla” model associated with Thomas Jackson and disseminated by the IAPC (Yarmel & Gregory, 2023)—the session employed an illustrated narrative stimulus, “Liam’s Secret Base,” followed by student-generated dialogue and a collaborative “Safe (...) Sharing Scenario” activity. These components were designed to foster critical, creative, and caring thinking (Lipman, 2003) about digital life. This article demonstrates that engaging young learners in ethical inquiry about AI technologies is both timely—given the global call for age-appropriate AI-ethics education (UNICEF, 2021; Dabbagh et al., 2025)—and feasible, since most children already interact with AI-mediated platforms. Classroom reflections showed that the lesson supported higher-order reasoning, metacognitive awareness, and collaborative dialogue in a supportive environment, outcomes consistent with reported benefits of P4C (Adams et al., 2023; Ab Wahab et al., 2022). Rather than prescribing a list of “don’ts,” educators used open-ended questions and realistic digital-life scenarios to nurture children’s autonomy in ethical decision-making, echoing participatory design approaches from the MIT Media Lab’s AI + Ethics curriculum (Wu, 2020; Williams et al., 2022). We conclude that P4C—co-founded by Matthew Lipman and Ann Margaret Sharp (Gregory & Laverty, 2017)—provides an effective pedagogical framework for integrating digital-ethics learning into childhood education, enabling students to become thoughtful, informed, and responsible digital citizens capable of navigating an increasingly data-driven world (Floridi, 2023/2024; Turner, 2019). (shrink)

Ethicists differ considerably in their reasons for using empirical data. This paper presents a brief overview of four traditional approaches to the use of empirical data: “the prescriptive applied ethicists,” “the theorists,” “the critical applied ethicists,” and “the particularists.” The main aim of this paper is to introduce a fifth approach of more recent date (i.e. “integrated empirical ethics”) and to offer some methodological directives for research in integrated empirical ethics. All five approaches are presented in a table (...) for heuristic purposes. The table consists of eight columns: “view on distinction descriptive-prescriptive sciences,” “location of moral authority,” “central goal(s),” “types of normativity,” “use of empirical data,” “method,” “interaction empirical data and moral theory,” and “cooperation with descriptive sciences.” Ethicists can use the table in order to identify their own approach. Reflection on these issues prior to starting research in empirical ethics should lead to harmonization of the different scientific disciplines and effective planning of the final research design. Integrated empirical ethics (IEE) refers to studies in which ethicists and descriptive scientists cooperate together continuously and intensively. Both disciplines try to integrate moral theory and empirical data in order to reach a normative conclusion with respect to a specific social practice. IEE is not wholly prescriptive or wholly descriptive since IEE assumes an interdepence between facts and values and between the empirical and the normative. The paper ends with three suggestions for consideration on some of the future challenges of integrated empirical ethics. (shrink)

The integration of information resources in the life sciences is one of the most challenging problems facing bioinformatics today. We describe how Language and Computing nv, originally a developer of ontology-based natural language understanding systems for the healthcare domain, is developing a framework for the integration of structured data with unstructured information contained in natural language texts. L&C’s LinkSuite™ combines the flexibility of a modular software architecture with an ontology based on rigorous philosophical and logical principles that (...) is designed to comprehend the basic formal relationships that structure both reality and the ways humans perceive and communicate about reality. (shrink)

Web-based platforms play an increasingly important role in managing and sharing research data of all types and sizes. This article presents a case study of the data storage, sharing, and management platform Figshare. We argue that such platforms are displacing and reconfiguring the infrastructure of norms, technologies, and institutions that underlies traditional scholarly communication. Using a theoretical framework that combines infrastructure studies with platform studies, we show that Figshare leverages the platform logic of core and complementary components to (...) re-integrate a presently splintered scholarly infrastructure. By means of this logic, platforms may provide the path to bring data inside a scholarly communication system still optimized mainly for text publications. Yet the platform strategy also risks turning over critical scientific functions to private firms whose longevity, openness, and corporate goals remain uncertain. It may amplify the existing trend of splintering infrastructures, with attendant effects on equity of service. (shrink)

We appreciate Chapman and colleagues’ (2025) advocacy for revising the Common Rule to address the downstream effects of data-centric research, particularly the potential for group harms such as sti...

Genomic data is generated, processed and analysed at an increasingly rapid pace. This data is not limited to the medical context, but plays an important role in other contexts in society, such as commercial DNA testing, the forensic setting, archaeological research, and genetic surveillance. Genomic information also crosses the borders of these domains, e.g. forensic use of medical genetic information, insurance use of medical genomic information, or research use of commercial genomic data. This paper (1) argues that (...) an informed consent approach for genomic information has limitations in many societal contexts, and (2) seeks to broaden the bioethical debate on genomic information by suggesting an approach that is applicable across multiple societal contexts. I argue that the contextual integrity framework, a theory rooted in information technology and big data ethics, is an effective tool to explore ethical challenges that arise from genomic information within a variety of different contexts. Rather than focusing on individual control over information, the contextual integrity approach holds that information should be shared and protected according to the norms that govern certain distinct social contexts. Several advantages of this contextual integrity approach will be discussed. The paper concludes that the contextual integrity framework helps to articulate and address a broad spectrum of ethical, social, and political factors in a variety of different societal contexts, while giving consideration to the interests of individuals, groups, and society at large. (shrink)

Industry 4.0 and digital technological innovations have facilitated faster, flexible, and efficient production, consumption, and transmission of information. Consequently, technological advancements such as Artificial Intelligence (AI), Blockchain Technologies (BT), and Business Data Analytics (BDA) are reshaping the daily tasks of accountants worldwide and the accounting field can capitalize and benefit from new opportunities. Notably, the accounting profession is rapidly evolving, as are the skills and careers of accounting professionals. In response to these changes, the accounting profession has developed competency (...) frameworks and career path publications to assist its members in adapting, addressing challenges, and seizing new opportunities. However, the university curriculum often introduces these technological advancements as separate modules rather than embedding them into the existing course content. Technology is reshaping accounting and educational institutions must equip students with the necessary skills to succeed in digital environments. By integrating digital technologies into the curriculum, students can acquire knowledge about modern accounting practices to meet the evolving needs of the industry. This study follows a qualitative research method that utilizes desktop research since it is theoretical in nature. It builds on existing research studies to explore the integration of AI, BT, and BDA into the University of Johannesburg’s (UJ) Commercial Accounting Diploma Curriculum. It also expands on the educational framework by proposing learning outcomes to be covered in the curriculum and providing teaching guidelines to support the new insertions. The study concludes that, the current curriculum does not integrate digital technologies and suggests the adoption of a new curriculum that addresses this gap. (shrink)

In this paper we discuss the process of building a joint probability distribution from an input set of low-dimensional probability distributions. Since the solution of the problem for a consistent input set of probability distributions is known we concentrate on a setup where the input probability distributions are inconsistent. In this case the iterative proportional fitting procedure (IPFP), which converges in the consistent case, tends to come to cycles. We propose a new algorithm that converges even in inconsistent case. The (...) important property of the algorithm is that it can be efficiently implemented exploiting decomposability of considered distributions. (shrink)

A course focusing on ethical issues in physics has been taught to undergraduate students at Eastern Michigan University since 1988. The course covers both responsible conduct of research and ethical issues associated with how physicists interact with the rest of society. Since most undergraduate physics majors will not have a career in academia, it is important that a course such as this address issues that will be relevant to physicists in a wide range of job situations. There is a wealth (...) of published work that can be drawn on for reading assignments. (shrink)

While teachers' knowledge is widely viewed as a key aspect of professional development in the new era, little research attention has been paid to one of its key components: teacher data literacy. Accordingly, this study aimed to combine teacher data literacy with TPACK, a widely-used framework for understanding and assessing teachers' knowledge. We first used qualitative methods to develop this integrated framework, then distributed a quantitative self-report survey based on the framework to teachers, and analyzed the resulting (...) class='Hi'>data. The qualitative phase highlighted five types of teachers' knowledge required in an integrated core knowledge system incorporating data literacy and provided insights for reflecting on teaching and learning in smart learning environments. The quantitative analysis of data from the TDL-TPACK questionnaire indicated that most teachers were competent practitioners but had some areas for improvement. Experienced teachers in their 30s and 40s performed at higher levels, while some of those aged over 50 displayed incremental decreases in performance. Other factors such as the age, experience, academic qualifications, and role of teachers may affect different aspects of their knowledge, including their data literacy. The research findings provide useful insights for additional teacher training and development programs in the context of smart education. (shrink)

Mechanistic models are built using knowledge as the primary information source, with well-established biological and physical laws determining the causal relationships within the model. Once the causal structure of the model is determined, parameters must be defined in order to accurately reproduce relevant data. Determining parameters and their values is particularly challenging in the case of models of pathophysiology, for which data for calibration is sparse. Multiple data sources might be required, and data may not be (...) in a uniform or desirable format. We describe a calibration strategy to address the challenges of scarcity and heterogeneity of calibration data. Our strategy focuses on parameters whose initial values cannot be easily derived from the literature, and our goal is to determine the values of these parameters via calibration with constraints set by relevant data. When combined with a covariance matrix adaptation evolution strategy (CMA-ES), this step-by-step approach can be applied to a wide range of biological models. We describe a stepwise, integrative and iterative approach to multiscale mechanistic model calibration, and provide an example of calibrating a pathophysiological lung adenocarcinoma model. Using the approach described here we illustrate the successful calibration of a complex knowledge-based mechanistic model using only the limited heterogeneous datasets publicly available in the literature. (shrink)

DNA barcodes, like traditional sources of taxonomic information, are potentially powerful heuristics in the identification of described species but require mindful analytical interpretation. The role of DNA barcoding in generating hypotheses of new taxa in need of formal taxonomic treatment is discussed, and it is emphasized that the recursive process of character evaluation is both necessary and best served by understanding the empirical mechanics of the discovery process. These undertakings carry enormous ramifications not only for the translation of DNA sequence (...) data into taxonomic information but also for our comprehension of the magnitude of species diversity and its disappearance. This paper examines the potential strengths and pitfalls of integrating DNA sequence data, specifically in the form of DNA barcodes as they are currently generated and analyzed, with taxonomic practice. (shrink)

Combining information from two or possibly several blocks of data is gaining increased attention and importance in several areas of science and industry. Typical examples can be found in chemistry, spectroscopy, metabolomics, genomics, systems biology and sensory science. Many methods and procedures have been proposed and used in practice. The area goes under different names: data integration, data fusion, multiblock analyses, multiset analyses and a few more. This book is an attempt to give an up-to-date treatment (...) of the most used and important methods within an important branch of the area; namely methods based on so-called components or latent variables. These methods have already obtained an enormous attention in for instance chemometrics, bioinformatics, machine learning and sensometrics and have proved to be important both for prediction and interpretation. The book is primarily a description of methodologies, but most of the methods will be illustrated by examples from the above-mentioned areas. The book is written such that both users of the methods as well as method developers will hopefully find sections of interest. In the end of the book there is a description of a software package developed particularly for the book. This package is freely available in R and covers many of the methods discussed. To distinguish the different type of methods from each other, the book is divided into five parts. Part I is introduction and preliminary concepts. Part II is the core of the book containing the main unsupervised and supervised methods. Part III deals with more complex structures and, finally, Part IV discusses alternative unsupervised and supervised methods. The book ends with Part V discussing the available software. Our recommendation for reading the book are as follows. A minimum read of the book would involve chapters 1, 2, 3, 5 and 7. Chapters 4, 6 and 8 are more specialized and chapters 9 and 10 contain methods we think are more advanced or less obvious to use. We feel privileged to have so many friendly colleagues who were willing to spend their time on helping us to improve the book by reading separate chapters. We would like to express our thanks to: Rasmus Bro, Margriet Hendriks, Ulf Indahl, Henk Kiers, Ingrid MaÌ⁽ge, Federico Marini, AÌ⁽smund Rinnan, Rosaria Romano, Lars Erik Solberg, Marieke Timmerman, Oliver Tomic, Johan Westerhuis and Barry Wise. Of course, the correctness of the final text is fully our responsibility! (shrink)

In this article, we discuss the methodological implications of data and theory integration for Theory-Based Evaluation (TBE). TBE is a family of approaches to program evaluation that use program theories as instruments to answer questions about whether, how, and why a program works. Some of the groundwork about TBE has expressed the idea that a proper program theory should specify the intervening mechanisms underlying the program outcome. In the present article, we discuss in what way data and (...) theory integration can help evaluators in constructing and refining mechanistic program theories. The paper argues that a mechanism is both a network of entities and activities and a network of counterfactual relations. Furthermore, we argue that although data integration typically provides information about different parts of a program, it is the integration of theory that provides the most important mechanistic insights. (shrink)

Susser and Cabrera (2024) assess the role of bespoke neuro-privacy regulations including the creation of a novel right to mental privacy. They argue that focusing on what distinguishes mental priva...

Specific classes of cyberspaces emphasize different types of digital transactions given the user’s context, thus making it essential to take into account what these environments can afford. In this way, we can portray the niches of technological use as ecologies of particular possibilities and compare how they differ between distinct spheres of human life. The present research is focused on describing the conceptual integration of a taxonomic crossover between Virtual Learning Environments and Educational Affordances of Technology, while also performing (...) empirical testing and determining the psychometric properties in a scale regarding the aforementioned taxonomy. The study sample consisted of 320 students in the departments of foreign languages from three different universities in Sonora. Students were given a questionnaire of 21 items organized into four subscales with a Likert-type response option to measure the notions concerning their usage of Virtual Learning Environments. Internal consistency procedures and confirmatory factor analysis by means of Cronbach’s alpha and Structural modeling support the derived factorial structure, which contains Cyber-Communications, Virtual Behavior Settings, Virtual Communities, and Availability and Access to Connectivity. This structure traces the environmental properties perceived by learners in a virtual environment. Results sustain the initial conceptual construction regarding the proposed taxonomy, conclude that the ‘Virtual Learning Environments Questionnaire’ demonstrates adequate psychometric properties, and validate it as a fitting measure to assess the perceived psychological experience of students in a digital educational setting. (shrink)

Integrative structure modeling is a method for using information from multiple sources to compute structural models of biomolecular systems. It proceeds via four steps: (i) defining the model representation, which determines the variables whose values will be computed; (ii) constructing a function for scoring alternative models according to how well they accommodate input information; (iii) searching a space of candidate models for acceptable models; and (iv) analyzing acceptable models to evaluate their fit with input information. These steps are iterated until (...) a model adequate for addressing biological questions is found. In this paper, we draw lessons from integrative modeling about effective integration and about modeling. We describe what it means to integrate information from multiple sources: Integration amounts to distributing information among the four steps of integrative modeling. Theory and data alike can be sources of information; this process thus generates models of information, rather than models of theory or models of data. We then propose heuristics for distributing information and designing multiple iterations of modeling. Effective iteration requires prioritizing the most reliable information and minimizing the time required to obtain an adequate model. Rather than being constructed from theory and assessed using data, models are constructed from any available information and assessed in a coherentist manner. (shrink)

We conducted an integrative data analysis to examine the hedonic character of nostalgia. We combined positive and negative affect measures from 41 experiments manipulating nostalgia ( N = 4,659). Overall, nostalgia inductions increased positive and ambivalent affect, but did not significantly alter negative affect. The magnitude of nostalgia’s effects varied markedly across different experimental inductions of the emotion. The hedonic character of nostalgia, then, depends on how the emotion is elicited and the benchmark (i.e., control condition) to which it (...) is compared. We discuss implications for theory and research on nostalgia and emotions in general. (shrink)

Using web standards, such as uniform resource identifiers (URIs), XML and HTTP, for naming and describing resources which are not information objects is the key difference between the Web as we know it today and the Semantic Web. Naming and interlinking this type of resources by HTTP URIs (instead of individual constants in a formal language) is the key feature which distinguishes traditional knowledge representation from web-scale knowledge representation. However, this use of URIs brought back attention to the old philosophical (...) problem of identity and reference in a new form. In this paper, we analyze the new version of the problem, provide a formal model for dealing with it when interlinking knowledge on the Web, and argue for the need of a distinction between the use of URIs for describing and accessing resources, and the use of URIs for fixing the reference. We show that in the current practice of linking data these roles are not clearly distinguished, and that this fact may cause unwanted effects and prevent some basic forms of data integration. We also discuss the role of an entity name system as a potential piece of infrastructure for fixing the reference in the Semantic Web. (shrink)

Initially the problems of data integration, for example in the field of medicine, were resolved in case by case fashion. Pairs of databases were cross-calibrated by hand, rather as if one were translating from French into Hebrew. As the numbers and complexity of database systems increased, the idea arose of streamlining these efforts by constructing one single benchmark taxonomy, as it were a central switchboard, into which all of the various classification systems would need to be translated only (...) once. By serving as a lingua franca for database integration this benchmark taxonomy would ensure that all databases calibrated in its terms would be automatically compatible with each other. We describe one strategy for creating such a lingua franca, in which philosophical ontology plays a central role. (shrink)

During Kant’s time, the fusion of mathematics and empirical research – pioneered by figures like Newton – was reshaping how conclusions about nature were drawn. Kant had to grapple with the question of how such validation depends on the logical frameworks and rationality standards used to structure scientific demonstrations. With the fading reliance on metaphysical principles to establish such standards, this article positions Kant’s theory of synthesis as a pivotal response to these philosophical challenges, foreshadowing theses on how representational systems (...) model and structure phenomena. A substantial part of Kant’s strategy involved reconciling the inferential and representational dimensions of knowledge. The article will show how Kant’s transcendental logic, Fodor’s Language of Thought, and Husserl’s formal ontologies collectively address the specification of non-extensional content by bridging conceptual mediators, representational structures, and grounded genus-species relationships. We then argue that without linking the formal specification of intensional cognitive processes to a normative theory of higher-order contents, and a theory of science and epistemology, the program remains incomplete. In chapter three, we delve into the debate with naturalist reductions of intentionality (Dennett) and with phenomenological and Hegelian perspectives, emphasizing normative, reflective, and intersubjective frameworks that shape cognition and its alignment with scientific paradigms and cultural norms. We conclude that Kant’s thesis of a priori syntheses anchors problematic representations within possible experience, offering a dynamic framework for scientific self-consciousness and judgment certainty, bringing forth phenomenological data conditioned by a normative ceiling. (shrink)

Data sharing between housing and education agencies will provide housing agencies with resources to assist them with efforts to decrease segregation and mitigate the adverse health outcomes experienced by people of color. The Fair Housing Act has the potential to fulfill its original integrationist purpose if housing and education agencies combine resources and data to create and implement fair housing plans. The Biden Administration’s restored rule to affirmatively further fair housing pursuant to the Fair Housing Act of 1968 (...) which seeks to reduce segregation and increase housing equity. However, it omits most of the processes set forth by the Obama Administration whereby federal agencies provide the proper tools to housing agencies so that they are able to make data-based decisions about housing policies. This article advocates for the sharing of data between housing and education agencies to optimize the positive impact of fair housing not only within housing, but also on the education, employment, and health opportunities for communities of color. (shrink)

Road link speed is one of the important indicators for traffic states. In order to incorporate the spatiotemporal dynamics and correlation characteristics of road links into speed prediction, this paper proposes a method based on LDA and GCN. First, we construct a trajectory dataset from map-matched GPS location data of taxis. Then, we use the LDA algorithm to extract the semantic function vectors of urban zones and quantify the spatial dynamic characteristics of road links based on taxi trajectories. Finally, (...) we add semantic function vectors to the dataset and train a graph convolutional network to learn the spatial and temporal dependencies of road links. The learned model is used to predict the future speed of road links. The proposed method is compared with six baseline models on the same dataset generated by GPS equipped on taxis in Shenzhen, China, and the results show that our method has better prediction performance when semantic zoning information is added. Both composite and single-valued semantic zoning information can improve the performance of graph convolutional networks by 6.46% and 8.35%, respectively, while the baseline machine learning models work only for single-valued semantic zoning information on the experimental dataset. (shrink)

Questions of data privacy in Africa are imbued with complexity. We examine a slice of this complexity by putting the concept of contextual integrity into dialogue with Africa's plural-legal contexts to explore data privacy within Africa's emerging digital landscape. The conceptual insights are empirically illustrated based on a case study in Ghana, involving content analysis of policy documents and interviews with a sample of residents, cultural leaders (among the Akan ethnic group), subject matter experts, and digital entrepreneurs (e.g. (...) fintech firms). Our findings illuminate a nuanced and contextually rooted understanding of privacy, focusing on the complementarities and tensions around data anonymization for privacy; the multiplicity of information spheres that result in a complicated terrain of privacy breaches; and the individuality, mutuality, and collectivity of privacy harms and remedies thereof. Our findings challenge prevailing discourses in the literature that either suggests (a) misalignments between locally-rooted privacy norms and European-imported statutory regulations on privacy in African countries or (b) privacy myopia among Africans—underestimating their personal information and the need to protect such information. We suggest shifting away from such privacy discourses from above (meta-narratives, generalizations, and inherent assumptions on African states and societies) to conversations from below. That is, refocusing on the particularities of place, culture, and norms that complicate meta-narratives on privacy. This requires centering the everyday privacy norms that result from co-existing, and sometimes oppositional, customary-informed and state-stipulated information norms within Africa's emerging digital landscapes. (shrink)

Here we present what we believe is a novel account of what languages are, along with an axiomatically rich representation of languages and language-related data that is based on this account. We propose an account of languages as aggregates of dispositions distributed across aggregates of persons, and in doing so we address linguistic competences and the processes that realize them. This paves the way for representing additional types of language-related entities. Like demographic data of other sorts, data (...) about languages may be of use to researchers in a number of areas, including biomedical research. Data on the languages used in clinical encounters are typically included in medical records, and capture an important factor in patient-provider interactions. Like many types of patient and demographic data, data on a person’s preferred and primary languages are organized in different ways by different systems. This can be a barrier to data integration. We believe that a robust framework for representing language in general and preferred and primary language in particular – which has been lacking in ontologies thus far – can promote more successful integration of language-related data from disparate data sources. (shrink)

Successful biomedical data mining and information extraction require a complete picture of biological phenomena such as genes, biological processes, and diseases; as these exist on different levels of granularity. To realize this goal, several freely available heterogeneous databases as well as proprietary structured datasets have to be integrated into a single global customizable scheme. We will present a tool to integrate different biological data sources by mapping them to a proprietary biomedical ontology that has been developed for the (...) purposes of making computers understand medical natural language. (shrink)