Big Data

This paper investigates the individual-level economic impacts of Marxist-Leninist indoctrination. We examine the long-run effects of a rogue educational experiment that, between 1973 and 1975, exposed two cohorts of fifth graders in one Finnish municipality to a history and social studies curriculum influenced by Soviet ideas. The experiment aimed to study the formation of a “functioning [socialist] worldview.” Students in other cohorts and municipalities followed the standard curriculum. Using comprehensive register data and a difference-in-differences approach, we show that exposed students (...) earned significantly less in adulthood than their non-exposed peers. This gap is partly explained by reduced labor supply and a shift toward more left-leaning, lower-paying occupations. We find no substantial effects on educational attainment, cognitive ability, or personality traits related to achievement striving. These findings highlight the long-lasting economic consequences of early exposure to ideological content in the classroom. (shrink)

Scientific data without uncertainty estimates are increasingly seen as incomplete. Recent discussions in the philosophy of data, however, have given little attention to the nature of uncertainty estimation. We begin to redress this gap by, first, discussing the concepts and practices of uncertainty estimation in metrology and showing how they can be adapted for scientific data more broadly; and second, advancing five philosophical theses about uncertainty estimates for data: they are substantive epistemic products; they are fallible; they can be iteratively (...) improved; they should be judged in terms of their adequacy-for-purpose; and these estimates, in turn, are essential for judging data adequacy. We illustrate these five theses using the example of the GISTEMP global temperature dataset. Our discussion introduces a novel adequacy-for-purpose view of uncertainty estimation, addresses a weakness in a recent philosophical account of data, and provides a new perspective on the “safety” versus “precision” debate in metrology. (shrink)

Alisa Bokulich and Wendy Parker (2021) provide an account of data modeling they call the pragmatic-representational view (PR view). According to this view, data models are akin to theoretical models in that they should be evaluated based on their adequacy for particular purposes. In this paper, I present a challenge for the PR view. I argue that a separation between data generators and users can prevent adequacy-for-purpose from being a good evaluative tool. I analyze an example from microbiome bioinformatics to (...) illustrate my critique of Bokulich and Parker’s view and then propose a tripartite disambiguation of the term ‘data model’. (shrink)

Bu makale, “generative artificial intelligence” kavramı için “türetici yapay zekâ” karşılığını önermekte ve bu önerinin kavramsal, tarihsel ve felsefi gerekçelerini ortaya koymaktadır. Tarihsel arka plan açısından makale, yapay zekâ araştırmalarının 1943'teki McCulloch-Pitts çalışmasından başlayarak 1956 Dartmouth Konferansı'na, oradan Birinci ve İkinci Yapay Zekâ Kışları'na uzanan sürecini incelemektedir. Sembolik yapay zekânın bilgi gösterimi, çerçeve problemi ve nitelik problemi gibi yapısal güçlükler karşısında yetersiz kalması; uzman sistemlerin çöküşü ve 21. yüzyılda büyük veri, derin öğrenme ve büyük dil modellerinin yükselişi kronolojik bir çerçevede (...) ele alınmaktadır. Makale, doğal zekâ ile türetici yapay zekâyı dört temel eksen üzerinden karşılaştırmaktadır: ortama adaptasyon, akılyürütme, öğrenme ve ustalaşma. Büyük dil modellerinin dilbilgisini ve mantığı kural sistemi olarak içselleştirmeksizin; yalnızca istatistiksel bir yan ürün olarak modellediği ve bu nedenle gerçek anlamda ne düşündüğü, ne anladığı ne de akılyürüttüğü ileri sürülmektedir. Türetici yapay zekânın metin, kod, görüntü ve video türetme gibi pratik olanakları değerlendirildikten sonra, bu teknolojinin beraberinde getirdiği tereddütler ele alınmaktadır. Makale, güçlü yapay zekânın mümkün olup olmadığı sorusuyla kapanmaktadır. Turing makinesinin teorik temelinin değişmediği, akılyürütmenin istatistikle ikame edilemediği ve insanın keşif ile özgün üretim kapasitesinin vektör temelli hesaplamayla karşılanamayacağı savunulmaktadır. Beyin, bilinç ve zekânın henüz yeterince tanımlanmamış kavramlar olduğu vurgulanarak, güçlü yapay zekânın bugün itibarıyla ne teknik ne teorik ne de kavramsal düzeyde gerçekleşmiş bir olgu olmadığı sonucuna ulaşılmaktadır. -/- Anahtar Kelimeler: Türetici yapay zekâ, büyük dil modelleri, Turing makinesi • örüntü türetme, sembolik yapay zekâ, akılyürütme, bilgi gösterimi, yapay genel zekâ -/- --- -/- This article proposes the term “türetici yapay zekâ” (derivational/generative artificial intelligence) as a Turkish equivalent for “generative artificial intelligence,” and elaborates the conceptual, historical, and philosophical grounds for this proposal. Historically, the article traces the development of artificial intelligence research from the 1943 McCulloch-Pitts paper through the 1956 Dartmouth Conference, the First and Second AI Winters, the rise and fall of expert systems and symbolic AI, and the emergence of big data, deep learning, and large language models in the twenty-first century. The structural failures of symbolic AI — including the frame problem, the qualification problem, and the limitations of knowledge representation projects such as Cyc — are examined in detail. The article compares natural intelligence with generative AI across four dimensions: environmental adaptation, reasoning, learning, and mastery. It argues that large language models model grammar and logic not as internalized rule systems but merely as statistical by-products of training, and that these systems therefore neither think, understand, nor reason in any meaningful sense. The outputs of such systems are determined not by intelligence but by statistics; not by understanding but by computation. After reviewing the practical capabilities of generative AI — text, code, image, and video generation — the article turns to its associated concerns: growing energy and hardware demands, data quality issues, corporate dependency, concentration of power, and the risk of instrumentalizing ethical discourse. The article concludes by examining whether strong AI, or artificial general intelligence, is achievable. It argues that the theoretical foundations of computation have not changed since Turing, that reasoning cannot be substituted by statistics, and that the human capacities for discovery and original creation cannot be replicated through vector-based statistical computation. Given that brain, consciousness, and intelligence remain insufficiently defined concepts, the article concludes that strong AI has not been achieved — technically, theoretically, or conceptually — and that meaningful progress toward it will require paradigms that go beyond the current computational framework. -/- Keywords: Generative artificial intelligence, large language models, Turing machine, pattern generation, symbolic artificial intelligence, reasoning, knowledge representation, artificial general intelligence. (shrink)

Data interoperability poses unique ethical challenges across a range of academic, industrial, and governmental implementations of data systems. Central to data interoperability is the design of systems and protocols for exchanging or integrating data from different initial source domains. Data interoperability is often regarded as necessary for carrying out tasks between different organizations and suborganizations as well as for ensuring secondary use of data for research purposes. However, interoperability poses a number of ethical problems whose contours can prove especially challenging (...) in comparison to how ethical harms take hold at other moments of the data life cycle (such as algorithmic processing or results dissemination). Taking biomedical data interoperability as a focal domain, this article provides an overview of data interoperability, maps the central ethical harms that may challenge interoperability projects, and proposes a response to these problems through an approach rooted in philosophical pragmatism. Pragmatist responses to both individual and structural harms of interoperability are presented through three companion strategies: shared standards, manual data curation, and meticulous data documentation. (shrink)

У монографії поглиблено науково-методологічні підходи та запропоновано практичні рекомендації щодо удосконалення управління та застосування управлінського інструментарію підвищення ринкової капіталізації машинобудівних підприємств на основі вартісної концепції. Розкрито теоретичні основи управління ринковою капіталізацією підприємства на основі вартісної концепції. Здійснено оцінювання вартості машинобудівних підприємств України, аналіз формування ринкової капіталізації та оцінювання впливу зміни складових вартості досліджуваних підприємств на її цільове значення. Удосконалено механізм управління ринковою капіталізацією підприємств на основі вартісної концепції, оцінено вплив стимулюючих інструментів на підвищення ринкової капіталізації, запропоновано систему управлінських рішень з (...) оптимізації структури капіталу та визначено її вплив на зміну вартості та ринкової капіталізації машинобудівних підприємств. Видання розраховане на наукових працівників, викладачів ЗВО, керівників підприємств, а також усіх, хто має намір поглибити знання з управління бізнесом. (shrink)

Despite ongoing debates over how best to understand the concept of and the right to privacy, we seem no closer to reaching agreement. These questions matter because it is often contested in many contemporary big data contexts whether a loss of privacy or a privacy rights violation has occurred; having a proper definition of the right to privacy will help us to clarify what justice requires. I suggest that the existing literature relies too heavily on thought experiments and intuitions in (...) defining the right to privacy. While this may be appropriate for defining the concept of privacy, we have other resources at our disposal when it comes to normatively defining the right to privacy, namely accounts of the values that underpin that right. I suggest that considering these values, which I take to be the capacity for relationships, non-domination and autonomy, gives us a reason to favour an account of the right to privacy understood as control over privacy. This consideration of values will also give us a distinctive conception of control which incorporates not only the ability to block access to information about oneself, but also some claims to disclose it where one chooses to. (shrink)

Background The collection, storage, and use of data are essential elements for advancing mental health services, clinical care, and policy. Data governance is the framework of rules and processes that guide decisions impacting how data are stored, accessed, and controlled, and is foundational for the ethical management of data overall. As digital data practices grow, governance models must reflect the values of different communities. This scoping review aims to (1) understand the values and principles that are important to youth in (...) the governance of their mental health data, (2) identify existing implementations of co-created frameworks of values and principles for data governance, and (3) explore opportunities for integration with existing data governance frameworks. Methods The recommended scoping review methodology by Arksey and O’Malley, alongside updated methodology by Peters et al., was followed. We searched four databases (Scopus, MEDLINE (Ovid), Embase (Ovid), and APA PsycINFO (Ovid)) on June 7, 2024 for records from 2013 to present. Results Of 23 included studies, only four explicitly explored youths’ preferences for mental health data use and co-creation of governance frameworks. None focused specifically on youths’ data-related values, and few addressed issues of data governance. Important themes emerged around privacy, trust, transparency, and control. Conclusion Findings suggest that urgent attention is required to improve data governance for youth receiving mental health services. In particular, the collaborative development of values and principles for governance frameworks of youth mental health data should be central when developing youth-centered services that collect and use these data. (shrink)

Digital technologies hold promise to modernize healthcare. Such opportunity should be leveraged also to address the needs of rapidly ageing populations. Against this backdrop, this paper examines the use of wearable devices for promoting healthy ageing. Previous work has assessed the prospects of digital technologies for health promotion and disease prevention in older adults. However, to our knowledge, ours is one of the first attempts to specifically address the use of wearables for healthy ageing, and to offer ethical insights for (...) assessing the prospects of leveraging wearable devices in this context. We provide an analysis of the considerable opportunities associated with the use of wearables for healthy ageing, with a focus on the five domains of intrinsic capacity: locomotion, sensory functions, psychological aspects, cognition, and vitality. We then highlight current limitations and ethical challenges of such approach to healthy ageing, including issues related to access, inclusion, privacy, surveillance, autonomy, and regulation. We conclude by discussing the implications of our analysis in light of current debates on the ethics of digital health, and suggest measures to address the identified challenges. (shrink)

When does more data help and when does it not in the sciences? In the past decade, this question has become central because of the phenomenon of Big Data. While these discussions started as a result of somewhat naive ideas that have been closely analyzed and mostly rejected in the philosophy of data, the question about the epistemic difference that more or less data makes still matters, especially in light of the impressive performance of data science and machine learning tools, (...) which seem to improve their outcomes when they are trained on large volumes of data. In several areas of the sciences, having more data is also connected to methodological and epistemic benefits and more generally something that research should strive toward. More data is often equated to better science: this elicits crucial questions about the epistemic value of the quantity of data. In this chapter, we discuss this problem in light of current discussions in the life and health sciences and the philosophy of data. (shrink)

This booklet on Active Assisted Living (AAL) technologies has been created as part of the GoodBrother COST Action, which has run from 2020 to 2024. COST Actions are European research programs that promote collaboration across borders, uniting researchers, professionals, and institutions to address key societal challenges. GoodBrother focused on ethical and privacy concerns surrounding video and audio monitoring in care settings. The aim was to ensure that while AAL technologies help older adults and vulnerable individuals, their privacy and data protection (...) rights remain a top priority. This booklet is designed to guide you through the role that AAL technologies play in improving the quality of life for older adults, caregivers, and people with disabilities. AAL technologies offer tools for those facing cognitive or physical challenges. They can enhance independence, assist with daily routines, and promote a safer living environment. However, the rise of these technologies also brings important questions about data protection and user autonomy. (shrink)

Este manual sobre tecnologías para un envejecimiento activo y saludable, también conocido como Vida Asistida Activa (Active Assisted Living – AAL en sus siglas en inglés), ha sido creado como parte de la Acción COST GoodBrother, que se ha llevado a cabo desde 2020 hasta 2024. Las Acciones COST son programas de investigación europeos que promueven la colaboración internacional, uniendo a investigadores, profesionales e instituciones para abordar desafíos sociales importantes. GoodBrother se ha centrado en las cuestiones éticas y de privacidad (...) relacionadas con la monitorización empleando cámaras y micrófonos en entornos de provisión de cuidados. El objetivo ha sido garantizar que, mientras las tecnologías AAL ayudan a las personas mayores y a los individuos vulnerables, sus derechos de privacidad y protección de datos se mantengan como una prioridad. Este manual está diseñado para guiar en el papel que juegan las tecnologías AAL en la mejora de la calidad de vida de personas mayores, sus cuidadores y personas con discapacidades. Las tecnologías AAL ofrecen herramientas para quienes enfrentan desafíos cognitivos o físicos. Pueden mejorar la independencia, asistir en las rutinas diarias y promover un entorno de vida más seguro. Sin embargo, el aumento de estas tecnologías también plantea preguntas importantes sobre la protección de datos y la autonomía del usuario. A~través de estudios de casos prácticos, este manual explora escenarios reales en los que se aplican las tecnologías AAL. Ofrecen información sobre sus beneficios y desafíos. Se discuten situaciones en las que personas mayores han recuperado su sentido de independencia, por ejemplo, mediante el uso de sistemas de detección de caídas o dispositivos de monitorización de salud. Estas historias ofrecen una perspectiva humana de la tecnología, mostrando cómo pueden mejorar el bienestar mientras plantean cuestiones sobre la privacidad y el uso ético. El manual también destaca la importancia de tomar decisiones informadas al elegir e implementar sistemas AAL. Se enfatiza la~necesidad de que estas tecnologías equilibren seguridad y conveniencia con respeto a los derechos individuales. Encontrará información sobre cuestiones clave, incluyendo cómo seleccionar dispositivos adecuados, gestionar la configuración de privacidad y garantizar que la tecnología se integre de manera fluida en la vida diaria. Además de estos casos de estudio, el manual aborda temas importantes, como la seguridad de los datos, las consideraciones éticas y el papel de las normativas europeas, como el Reglamento General de Protección de Datos (RGPD), en la protección de la privacidad del usuario. Comprender estos aspectos es esencial para asegurar que los sistemas AAL no solo sean efectivos, sino también respetuosos de los derechos y la dignidad de los usuarios. Este recurso está destinado a una amplia audiencia, incluidos usuarios finales, cuidadores, profesionales de la salud y responsables de políticas. Proporciona orientación práctica sobre cómo integrar las tecnologías AAL en entornos de cuidados mientras se protege la privacidad y se garantiza un uso ético. Las ideas presentadas aquí buscan empoderar a los usuarios y cuidadores para que tomen decisiones informadas que mejoren tanto la calidad del cuidado como el respeto por la autonomía personal. (shrink)

În lumea de astăzi interconectată digital, amenințarea atacurilor cibernetice a devenit o preocupare esențială pentru indivizi, companii și guverne deopotrivă. Atacurile cibernetice reprezintă un risc semnificativ pentru confidențialitatea datelor, stabilitatea financiară, securitatea națională și chiar siguranța personală. Pe măsură ce sofisticarea și frecvența amenințărilor cibernetice continuă să crească, este imperativ să implementăm sisteme de apărare robuste pentru a ne proteja activele și infrastructura digitale. Tehnologia și metodele tradiționale de securitate sunt ineficiente în detectarea sau atenuarea atacurilor cibernetice. Acest eseu explorează (...) diverse strategii și măsuri de apărare împotriva atacurilor cibernetice. (shrink)

Training artificial intelligence (AI) systems requires vast quantities of data, and AI developers face a variety of barriers to accessing the information they need. Synthetic data has captured researchers’ and industry’s imagination as a potential solution to this problem. While some of the enthusiasm for synthetic data may be warranted, in this short paper we offer critical counterweight to simplistic narratives that position synthetic data as a cost-free solution to every data-access challenge—provocations highlighting ethical, political, and governance issues the use (...) of synthetic data can create. We question the idea that synthetic data, by its nature, is exempt from privacy and related ethical concerns. We caution that framing synthetic data in binary opposition to “real” measurement data could subtly shift the normative standards to which data collectors and processors are held. And we argue that by promising to divorce data from its constituents—the people it represents and impacts—synthetic data could create new obstacles to democratic data governance. (shrink)

This paper addresses a relatively new mode of ecological research: data synthesis studies. Data synthesis studies involve reusing data to create a general model as well as a reusable, aggregated dataset. Using a case from movement ecology, I analyse the trade-offs and strategies involved in data synthesis. Like theoretical ecological modelling, I find that synthesis studies involve a modelling trade-off between generality, precision and realism; they deal with this trade-off by adopting a pragmatic kludging strategy. I also identify an additional (...) trade-off, the synthesis trade-off, between making data easy to synthesise for a particular project, on the one hand, and facilitating data reuse for other projects, on the other. In response to this synthesis trade-off, researchers create flexible datasets that are relatively easy to use for particular projects and can be adjusted to suit some other purposes. The flexibility compromise is also found in broader open data efforts, making it a significant element in the future of data-intensive ecology. (shrink)

The fundamental algorithm in Nature: If zero then one, and, simultaneously, if one then zero. Basis for code and coding (all languages). Technically, then, 50 + 50 is 50.

The word “data” is everywhere in current discussions of science. How we store and share things people label“data” has become a central concern for the Open Science movement, for example, and the National Science Foundation and National Institutes of Health have invested billions of dollars to create publicly accessible databases as a major new source of intellectual capital in science and industry. What is new here? Has there been a shift in what“data” means that is key to understanding the future (...) of science? Sabina Leonelli’s new book, Data-Centric Biology: A Philosophical Study, argues for an important and fruitful answer outside the comfort zone of many philosophers. Along the way, she also delivers valuable insights into expanding efforts to standardize, automate, and communicate how scientists handle, share, reproduce, interpret, and store data. From the start, Leonelli rejects the idea that we can understand the significance of data for science in terms of intrinsic properties data possess as material traces of past processes. Similarly, the changes we are seeing in science are not driven simply by revolutionary technologies or methods.“The real source of innovation in current biology is the attention paid to data handling and dissemination practices and the ways in which such practices mirror economic and political modes of interaction and decision making” (1). (shrink)

DNA sequencers, Twitter, MRIs, Facebook, particle accelerators, Google Books, radio telescopes, Tumblr: what do these things have in common? According to the evangelists of “data science,” all of these are instruments for observing reality at unprecedentedly large scales and fine granularities. This perspective ignores the social reality of these very different technological systems, ignoring how they are made, how they work, and what they mean in favor of an exclusive focus on what they generate: Big Data. But no data, big (...) or small, can be interpreted without an understanding of the process that generated them. Statistical data science is applicable to systems that have been designed as scientific instruments, but is likely to lead to confusion when applied to systems that have not. In those cases, a historical inquiry is preferable. (shrink)