Results for 'big data'

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  1. Taxonomy for Humans or Computers? Cognitive Pragmatics for Big Data.Beckett Sterner & Nico M. Franz - 2017 - Biological Theory 12 (2):99-111.
    Criticism of big data has focused on showing that more is not necessarily better, in the sense that data may lose their value when taken out of context and aggregated together. The next step is to incorporate an awareness of pitfalls for aggregation into the design of data infrastructure and institutions. A common strategy minimizes aggregation errors by increasing the precision of our conventions for identifying and classifying data. As a counterpoint, we argue that there are (...)
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  2. Big Data and Changing Concepts of the Human.Carrie Figdor - 2019 - European Review 27 (3):328-340.
    Big Data has the potential to enable unprecedentedly rigorous quantitative modeling of complex human social relationships and social structures. When such models are extended to nonhuman domains, they can undermine anthropocentric assumptions about the extent to which these relationships and structures are specifically human. Discoveries of relevant commonalities with nonhumans may not make us less human, but they promise to challenge fundamental views of what it is to be human.
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  3. Big Data, Epistemology and Causality: Knowledge in and Knowledge Out in EXPOsOMICS.Stefano Canali - 2016 - Big Data and Society 3 (2).
    Recently, it has been argued that the use of Big Data transforms the sciences, making data-driven research possible and studying causality redundant. In this paper, I focus on the claim on causal knowledge by examining the Big Data project EXPOsOMICS, whose research is funded by the European Commission and considered capable of improving our understanding of the relation between exposure and disease. While EXPOsOMICS may seem the perfect exemplification of the data-driven view, I show how causal (...)
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  4. Engaging the Public in Ethical Reasoning About Big Data.Justin Anthony Knapp - 2016 - In Soren Adam Matei & Jeff Collman (eds.), Ethical Reasoning in Big Data: An Exploratory Analysis. Springer. pp. 43-52.
    The public constitutes a major stakeholder in the debate about, and resolution of privacy and ethical The public constitutes a major stakeholder in the debate about, and resolution of privacy and ethical about Big Data research seriously and how to communicate messages designed to build trust in specific big data projects and the institution of science in general. This chapter explores the implications of various examples of engaging the public in online activities such as Wikipedia that contrast with (...)
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  5.  57
    Big Data Ethics in Research.Nicolae Sfetcu - manuscript
    The main problems faced by scientists in working with Big Data sets, highlighting the main ethical issues, taking into account the legislation of the European Union. After a brief Introduction to Big Data, the Technology section presents specific research applications. There is an approach to the main philosophical issues in Philosophical Aspects, and Legal Aspects with specific ethical issues in the EU Regulation on the protection of natural persons with regard to the processing of personal data and (...)
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  6.  69
    Ethics of Identity in the Time of Big Data.James Brusseau - 2019 - First Monday 24 (5-6):00-11.
    Compartmentalizing our distinct personal identities is increasingly difficult in big data reality. Pictures of the person we were on past vacations resurface in employers’ Google searches; LinkedIn which exhibits our income level is increasingly used as a dating web site. Whether on vacation, at work, or seeking romance, our digital selves stream together. One result is that a perennial ethical question about personal identity has spilled out of philosophy departments and into the real world. Ought we possess one, unified (...)
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  7.  16
    Etica Big Data în cercetare.Nicolae Sfetcu - manuscript
    Principalele probleme cu care se confruntă oamenii de știință în lucrul cu seturile mari de date (Big Data), evidențiind principale aspecte etice, luând în considerare inclusiv legislația din Uniunea Europeană. După o scurtă Introducere despre Big Data, secțiunea Tehnologia prezintă aplicațiile specifice în cercetare. Urmează o abordare a principalelor probleme filosofice specifice în Aspecte filosofice, și Aspecte legale cu evidențierea problemelor etice specifice din Regulamentul UE privind protecția datelor 2016/679 (General Data Protection Regulation, "GDPR"). Secțiunea Probleme etice (...)
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  8.  15
    Big Data - Aspecte filosofice.Nicolae Sfetcu - manuscript
    Big Data poate genera, prin inferențe, noi cunoașteri și perspective. Paradigma care rezultă din utilizarea Big Data generează noi oportunități. Un motiv de îngrijorare majoră în cazul Big Data se datorează faptului că oamenii de știință de date tind să lucreze cu date despre subiectele pe care nu le cunosc și cu care nu au fost niciodată în contact, fiind înstrăinați de produsul final al activității lor (aplicarea analizelor). Un studiu recent afirmă că ceasta poate fi motivul (...)
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  9.  13
    Aspecte legale în lucrul cu Big Data.Nicolae Sfetcu - manuscript
    Utilizarea Big Data prezintă probleme juridice semnificative, în special din punctul de vedere al protecției datelor. Cadrul juridic existent al Uniunii Europene, bazat în special pe Directiva nr. 46/95/CE și Regulamentul general privind protecția datelor cu caracter personal, oferă o protecție corespunzătoare. Dar, pentru Big Data este necesară o strategie cuprinzătoare și globală. Evoluția în timp a fost de la dreptul de a exclude pe alții la dreptul la controlul propriilor date și, în prezent, la regândirea dreptului la (...)
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  10.  10
    Probleme etice în lucrul cu Big Data.Nicolae Sfetcu - manuscript
    Etica Big Data presupune aderarea la conceptele de comportament corect și greșit în ceea ce privește datele, în special datele cu caracter personal. Etica Big Data pune accentul pe colectorii și diseminatorii de date structurate sau nestructurate. Etica Big Data este susținută, la nivelul UE, de o amplă documentație, prin care se încearcă să se găsească soluții concrete pentru maximizarea valorii Big Data fără a sacrifica drepturile fundamentale ale omului. Autoritatea Europeană pentru Protecția Datelor (AEPD) sprijină (...)
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  11.  15
    Big Data.Nicolae Sfetcu - manuscript
    Termenul Big Data se referă la extragerea, manipularea și analiza unor seturi de date care sunt prea mari pentru a fi tratate în mod obișnuit. Din această cauză se utilizează software special și, în multe cazuri, și calculatoare și echipamente hardware special dedicate. În general la aceste date analiza se face statistic. Pe baza analizei datelor respective se fac de obicei predicții ale unor grupuri de persoane sau alte entități, pe baza comportamentului acestora în diverse situații și folosind tehnici (...)
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  12.  12
    Procesarea Big Data.Nicolae Sfetcu - manuscript
    Datele trebuie procesate cu instrumente avansate de colectare și analiză, pe baza unor algoritmi prestabiliți, pentru a putea obține informații relevante. Algoritmii trebuie să ia în considerare și aspecte invizibile pentru percepțiile directe. Big Data în procesele guvernamentale cresc eficiența costurilor, productivitatea și inovația. Registrele civile sunt o sursă pentru Big Data. Datele prelucrate ajută în domenii critice de dezvoltare, cum ar fi îngrijirea sănătății, ocuparea forței de muncă, productivitatea economică, criminalitatea, securitatea și gestionarea dezastrelor naturale și a (...)
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  13.  51
    No Wisdom in the Crowd: Genome Annotation at the Time of Big Data - Current Status and Future Prospects.Antoine Danchin - 2018 - Microbial Biotechnology 11 (4):588-605.
    Science and engineering rely on the accumulation and dissemination of knowledge to make discoveries and create new designs. Discovery-driven genome research rests on knowledge passed on via gene annotations. In response to the deluge of sequencing big data, standard annotation practice employs automated procedures that rely on majority rules. We argue this hinders progress through the generation and propagation of errors, leading investigators into blind alleys. More subtly, this inductive process discourages the discovery of novelty, which remains essential in (...)
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  14.  14
    Big Data.Nicolae Sfetcu - 2019 - Drobeta Turnu Severin: MultiMedia Publishing.
    Odată cu creșterea volumului de date pe Internet, în media socială, cloud computing, dispozitive mobile și date guvernamentale, Big Data devine în același timp o amenințare și o oportunitate în ceea ce privește gestionarea și utilizarea acestor date, menținând în același timp drepturile persoanelor implicate. În fiecare zi, folosim și generăm tone de date, alimentând bazele de date ale agențiilor guvernamentale, companiilor private și chiar cetățenilor privați. Beneficiem în multe feluri de existența și utilizarea Big Data, dar trebuie (...)
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  15.  94
    A Survey of Business Intelligence Solutions in Banking Industry and Big Data Applications.Elaheh Radmehr & Mohammad Bazmara - 2017 - International Journal of Mechatronics, Electrical and Computer Technology 7 (23):3280-3298.
    Nowadays, the economic and social nature of contemporary business organizations chiefly banks binds them to face with the sheer volume of data and information and the key to commercial success in this area is the proper use of data for making better, faster and flawless decisions. To achieve this goal organizations requires strong and effective tools to enable them in automating task analysis, decision-making, strategy formulation and risk prediction to prevent bankruptcy and fraud .Business Intelligence is a set (...)
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  16.  15
    An Ethics Framework for Big Data in Health and Research.Vicki Xafis, G. Owen Schaefer, Markus K. Labude, Iain Brassington, Angela Ballantyne, Hannah Yeefen Lim, Wendy Lipworth, Tamra Lysaght, Cameron Stewart, Shirley Sun, Graeme T. Laurie & E. Shyong Tai - 2019 - Asian Bioethics Review 11 (3):227-254.
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  17.  14
    Precision Medicine and Big Data.G. Owen Schaefer, E. Shyong Tai & Shirley Sun - 2019 - Asian Bioethics Review 11 (3):275-288.
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  18.  38
    Ethics of Identity in the Time of Big Data (Conference Paper).James Brusseau - manuscript
    According to Facebook’s Mark Zuckerberg, big data reality means, “The days of having a different image for your co-workers and for others are coming to an end, which is good because having multiple identities represents a lack of integrity.” Two sets of questions follow. One centers on technology and asks how big data mechanisms collapse our various selves (work-self, family-self, romantic-self) into one personality. The second question set shifts from technology to ethics by asking whether we want the (...)
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  19.  36
    Gründe Geben. Maschinelles Lernen Als Problem der Moralfähigkeit von Entscheidungen. Ethische Herausforderungen von Big-Data.Andreas Kaminski, Michael Nerurkar, Christian Wadephul & Klaus Wiegerling (eds.) - forthcoming - Bielefeld: Springer.
    Entscheidungen verweisen in einem begrifflichen Sinne auf Gründe. Entscheidungssysteme bieten eine probabilistische Verlässlichkeit als Rechtfertigung von Empfehlungen an. Doch nicht für alle Situationen mögen Verlässlichkeitsgründe auch angemessene Gründe sein. Damit eröffnet sich die Idee, die Güte von Gründen von ihrer Angemessenheit zu unterscheiden. Der Aufsatz betrachtet an einem Beispiel, einem KI-Lügendetektor, die Frage, ob eine (zumindest aktuell nicht gegebene) hohe Verlässlichkeit den Einsatz rechtfertigen kann. Gleicht er nicht einem Richter, der anhand einer Statistik Urteile fällen würde?
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  20. Big Data Optimization in Machine Learning.Xiaocheng Tang - 2015 - Disertation 1.
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  21.  22
    Data Analytics in Higher Education: Key Concerns and Open Questions.Alan Rubel & Kyle M. L. Jones - 2017 - University of St. Thomas Journal of Law and Public Policy 1 (11):25-44.
    “Big Data” and data analytics affect all of us. Data collection, analysis, and use on a large scale is an important and growing part of commerce, governance, communication, law enforcement, security, finance, medicine, and research. And the theme of this symposium, “Individual and Informational Privacy in the Age of Big Data,” is expansive; we could have long and fruitful discussions about practices, laws, and concerns in any of these domains. But a big part of the audience (...)
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  22. Political Communication in Social Networks Election Campaigns and Digital Data Analysis: A Bibliographic Review.Luca Corchia - 2019 - Rivista Trimestrale di Scienza Dell’Amministrazione (2):1-50.
    The outcomes of a bibliographic review on political communication, in particular electoral communication in social networks, are presented here. The electoral campaigning are a crucial test to verify the transformations of the media system and of the forms and uses of the linguistic acts by dominant actors in public sphere – candidates, parties, journalists and Gatekeepers. The aim is to reconstruct the first elements of an analytical model on the transformations of the political public sphere, with which to systematize the (...)
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  23.  38
    Are Big Gods a Big Deal in the Emergence of Big Groups?Quentin D. Atkinson, Andrew James Latham & Joseph Watts - 2015 - Religion, Brain and Behavior 5 (4):266-274.
    In Big Gods, Norenzayan (2013) presents the most comprehensive treatment yet of the Big Gods question. The book is a commendable attempt to synthesize the rapidly growing body of survey and experimental research on prosocial effects of religious primes together with cross-cultural data on the distribution of Big Gods. There are, however, a number of problems with the current cross-cultural evidence that weaken support for a causal link between big societies and certain types of Big Gods. Here we attempt (...)
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  24.  99
    Introduction to Data Ethics.James Brusseau - 2018 - In The Business Ethics Workshop, 3rd Edition. Boston, USA: Boston Academic Publishing / Flatworld Knowledge. pp. 349-376.
    An Introduction to data ethics, focusing on questions of privacy and personal identity in the economic world as it is defined by big data technologies, artificial intelligence, and algorithmic capitalism. -/- Originally published in The Business Ethics Workshop, 3rd Edition, by Boston Acacdemic Publishing / FlatWorld Knowledge.
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  25. Would You Mind Being Watched by Machines? Privacy Concerns in Data Mining.Vincent C. Müller - 2009 - AI and Society 23 (4):529-544.
    "Data mining is not an invasion of privacy because access to data is only by machines, not by people": this is the argument that is investigated here. The current importance of this problem is developed in a case study of data mining in the USA for counterterrorism and other surveillance purposes. After a clarification of the relevant nature of privacy, it is argued that access by machines cannot warrant the access to further information, since the analysis will (...)
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  26. Data Science and Mass Media: Seeking a Hermeneutic Ethics of Information.Christine James - 2015 - Proceedings of the Society for Phenomenology and Media, Vol. 15, 2014, Pages 49-58 15 (2014):49-58.
    In recent years, the growing academic field called “Data Science” has made many promises. On closer inspection, relatively few of these promises have come to fruition. A critique of Data Science from the phenomenological tradition can take many forms. This paper addresses the promise of “participation” in Data Science, taking inspiration from Paul Majkut’s 2000 work in Glimpse, “Empathy’s Impostor: Interactivity and Intersubjectivity,” and some insights from Heidegger’s "The Question Concerning Technology." The description of Data Science (...)
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  27.  67
    From Public Data to Private Information: The Case of the Supermarket.Vincent C. Müller - 2009 - In Maria Bottis (ed.), Proceedings of the 8th International Conference Computer Ethics: Philosophical Enquiry. Corfu, Greece: Nomiki Bibliothiki. pp. 500-507.
    The background to this paper is that in our world of massively increasing personal digital data any control over the data about me seems illusionary – informational privacy seems a lost cause. On the other hand, the production of this digital data seems a necessary component of our present life in the industrialized world. A framework for a resolution of this apparent dilemma is provided if by the distinction between (meaningless) data and (meaningful) information. I argue (...)
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  28.  27
    What to Do When Privacy Is Gone.James Brusseau - 2019 - In Computer Ethics - Philosophical Enquiry (CEPE) Proceedings. Norfolk, VA, USA: pp. 1 - 8.
    Today’s ethics of privacy is largely dedicated to defending personal information from big data technologies. This essay goes in the other direction; it considers the struggle to be lost, and explores two strategies for living after privacy is gone. First, total exposure embraces privacy’s decline, and then contributes to the process with transparency. All personal information is shared without reservation. The resulting ethics is explored through a big data version of Robert Nozick’s Experience Machine thought experiment. Second, transient (...)
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  29.  83
    The Orbital Space Environment and Space Situational Awareness Domain Ontology – Towards an International Information System for Space Data.Robert J. Rovetto - 2016 Sept - In Proceedings of The Advanced Maui Optical and Space Surveillance Technologies (AMOS) Conference.
    The orbital space environment is home to natural and artificial satellites, debris, and space weather phenomena. As the population of orbital objects grows so do the potential hazards to astronauts, space infrastructure and spaceflight capability. Orbital debris, in particular, is a universal concern. This and other hazards can be minimized by improving global space situational awareness (SSA). By sharing more data and increasing observational coverage of the space environment we stand to achieve that goal, thereby making spaceflight safer and (...)
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  30. The Threat of Algocracy: Reality, Resistance and Accommodation.John Danaher - 2016 - Philosophy and Technology 29 (3):245-268.
    One of the most noticeable trends in recent years has been the increasing reliance of public decision-making processes on algorithms, i.e. computer-programmed step-by-step instructions for taking a given set of inputs and producing an output. The question raised by this article is whether the rise of such algorithmic governance creates problems for the moral or political legitimacy of our public decision-making processes. Ignoring common concerns with data protection and privacy, it is argued that algorithmic governance does pose a significant (...)
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  31.  27
    What to Do When Privacy Is Gone.James Brusseau - 2019 - In D. E. Wittkower (ed.), Computer Ethics - Philosophical Enquiry (CEPE) Proceedings. Norfolk, VA, USA: Old Dominion. pp. 2-8.
    Today’s ethics of privacy is largely dedicated to defending personal information from big data technologies. This essay goes in the other direction; it considers the struggle to be lost, and explores two strategies for living after privacy is gone. First, total exposure embraces privacy’s decline, and then contributes to the process with transparency. All personal information is shared without reservation. The resulting ethics is explored through a big data version of Robert Nozick’s Experience Machine thought experiment. Second, transient (...)
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  32. Sentiment Analysis on Online Social Network.Vijaya Abhinandan - forthcoming - International Journal of Computer Science, Information Technology, and Security.
    A large amount of data is maintained in every Social networking sites.The total data constantly gathered on these sites make it difficult for methods like use of field agents, clipping services and ad-hoc research to maintain social media data. This paper discusses the previous research on sentiment analysis.
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  33.  32
    Data Mining the Brain to Decode the Mind.Daniel Weiskopf - forthcoming - In Neural Mechanisms: New Challenges in the Philosophy of Neuroscience.
    In recent years, neuroscience has begun to transform itself into a “big data” enterprise with the importation of computational and statistical techniques from machine learning and informatics. In addition to their translational applications such as brain-computer interfaces and early diagnosis of neuropathology, these tools promise to advance new solutions to longstanding theoretical quandaries. Here I critically assess whether these promises will pay off, focusing on the application of multivariate pattern analysis (MVPA) to the problem of reverse inference. I argue (...)
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  34.  60
    Commentary to "Turning Virtual Public Spaces Into Laboratories".Mark Tunick - 2014 - Analyses of Social Issues and Public Policy 14 (1):371-73.
    Evaluates a criticism based on privacy and other ethical grounds of Bond's study using 61 million persons on Facebook to determine whether political mobilization messages shared on social media can influence voting behavior.
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  35.  40
    Trusting in Others’ Biases: Fostering Guarded Trust in Collaborative Filtering and Recommender Systems.Jo Ann Oravec - 2004 - Knowledge, Technology & Policy 17 (3-4):106-123.
    Collaborative filtering is being used within organizations and in community contexts for knowledge management and decision support as well as the facilitation of interactions among individuals. This article analyzes rhetorical and technical efforts to establish trust in the constructions of individual opinions, reputations, and tastes provided by these systems. These initiatives have some important parallels with early efforts to support quantitative opinion polling and construct the notion of “public opinion.” The article explores specific ways to increase trust in these systems, (...)
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  36.  84
    Presentación. PolíTICa: Redes, Deliberación y Heurísticas Sociales. Dilemata. Revista Internacional de Éticas Aplicadas (22):I-Iv (2016) (Editora Invitada).María G. Navarro - 2016
    In the last forty years the number of specialized publications on deliberative democracy has increased steadily. Yet, today, one of the greatest challenges we still face today is to deepen into the knowledge of our actual and singular deliberative cultures. In order to achieve this, it is necessary that we use theoretical and methodological approaches that enable us to capture the inherent complexity to the specific forms of deliberation that are present in as different areas as that of politics, economics, (...)
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  37.  97
    Digitale Entmündigung und User Experience Design. Wie digitale Geräte uns nudgen, tracken und zur Unwissenheit erziehen.Rainer Mühlhoff - 2018 - Leviathan - Berliner Zeitschrift Für Sozialwissenschaft 46 (4).
    Der vorliegende Artikel untersucht moderne Mensch-Maschine-Interaktion im Kontext verbreiteter Hard- und Softwareoberflächen und diskutiert davon ausgehend die Frage nach Aufklärung und Gegenaufklärung im digitalen Zeitalter. Er nimmt das Feld des »User Experience Designs« in den Blick - dies ist ein stilprägender Fachdiskurs, in dem verhaltenswissenschaftliche Erkenntnisse und massendatenbasierte Analysen zur Optimierung von Benutzeroberflächen und Interaktionsdesigns eingesetzt werden. Anhand von Beispielstudien wird argumentiert, dass dieser Gestaltung systematisch drei implizite anthropologische Annahmen zugrunde liegen: Nutzerverhalten gilt als durch prä-reflexive Stimuli beeinflussbar; es gilt (...)
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  38.  71
    Digital Subjectivation and Financial Markets: Criticizing Social Studies of Finance with Lazzarato.Tim Christiaens - 2016 - Big Data and Society 3 (2):1-15.
    The recently rising field of Critical Data Studies is still facing fundamental questions. Among these is the enigma of digital subjectivation. Who are the subjects of Big Data? A field where this question is particularly pressing is finance. Since the 1990s traders have been steadily integrated into computerized data assemblages, which calls for an ontology that eliminates the distinction between human sovereign subjects and non-human instrumental objects. The latter subjectivize traders in pre-conscious ways, because human consciousness runs (...)
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  39.  40
    Orbital Space Environment and Space Situational Awareness Domain Ontology.Robert J. Rovetto - 2016 - In Stefano Borgo, Jean-Rémi Bourguet & Adrien Barton (eds.), CEUR workshop proceedings of The Joint Ontology Workshops, with the 9th International Conference of Formal Ontology for Information Systems (FOIS), Early Career Symposium. CEUR Scientific Workshops.
    A short summary paper of my Orbital Space Domain Ontology project (purl.org/space-ontology), originally conceived in 2011. Since then I've sought (without success) opportunities to realize it (either as a PhD or other degree thesis; or in an employment position) toward my original passion of entering the space sector and gaining further space education. Since then persons in the relevant space disciplines have seen the potential in it, and unfortunately some have taken advantage of my ideas yet excluded me from work. (...)
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  40. The Ethics of Algorithms: Mapping the Debate.Brent Mittelstadt, Patrick Allo, Mariarosaria Taddeo, Sandra Wachter & Luciano Floridi - 2016 - Big Data and Society 3 (2).
    In information societies, operations, decisions and choices previously left to humans are increasingly delegated to algorithms, which may advise, if not decide, about how data should be interpreted and what actions should be taken as a result. More and more often, algorithms mediate social processes, business transactions, governmental decisions, and how we perceive, understand, and interact among ourselves and with the environment. Gaps between the design and operation of algorithms and our understanding of their ethical implications can have severe (...)
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  41.  62
    Modeling of Biological and Social Phases of Big History.Leonid Grinin, Andrey V. Korotayev & Alexander V. Markov - 2015 - In Leonid Grinin & Andrey Korotayev (eds.), Evolution: From Big Bang to Nanorobots. Volgograd,Russia: Uchitel Publishing House. pp. 111-150.
    In the first part of this article we survey general similarities and differences between biological and social macroevolution. In the second (and main) part, we consider a concrete mathematical model capable of describing important features of both biological and social macroevolution. In mathematical models of historical macrodynamics, a hyperbolic pattern of world population growth arises from non-linear, second-order positive feedback between demographic growth and technological development. Based on diverse paleontological data and an analogy with macrosociological models, we suggest that (...)
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  42. Social Epistemology as a New Paradigm for Journalism and Media Studies.Yigal Godler, Zvi Reich & Boaz Miller - forthcoming - New Media and Society.
    Journalism and media studies lack robust theoretical concepts for studying journalistic knowledge ‎generation. More specifically, conceptual challenges attend the emergence of big data and ‎algorithmic sources of journalistic knowledge. A family of frameworks apt to this challenge is ‎provided by “social epistemology”: a young philosophical field which regards society’s participation ‎in knowledge generation as inevitable. Social epistemology offers the best of both worlds for ‎journalists and media scholars: a thorough familiarity with biases and failures of obtaining ‎knowledge, and a (...)
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  43. Democratizing Algorithmic Fairness.Pak-Hang Wong - forthcoming - Philosophy and Technology:1-20.
    Algorithms can now identify patterns and correlations in the (big) datasets, and predict outcomes based on those identified patterns and correlations with the use of machine learning techniques and big data, decisions can then be made by algorithms themselves in accordance with the predicted outcomes. Yet, algorithms can inherit questionable values from the datasets and acquire biases in the course of (machine) learning, and automated algorithmic decision-making makes it more difficult for people to see algorithms as biased. While researchers (...)
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  44. Beneficial Artificial Intelligence Coordination by Means of a Value Sensitive Design Approach.Steven Umbrello - 2019 - Big Data and Cognitive Computing 3 (1):5.
    This paper argues that the Value Sensitive Design (VSD) methodology provides a principled approach to embedding common values in to AI systems both early and throughout the design process. To do so, it draws on an important case study: the evidence and final report of the UK Select Committee on Artificial Intelligence. This empirical investigation shows that the different and often disparate stakeholder groups that are implicated in AI design and use share some common values that can be used to (...)
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  45. Why Do We Need to Employ Bayesian Statistics and How Can We Employ It in Studies of Moral Education?: With Practical Guidelines to Use JASP for Educators and Researchers.Hyemin Han - 2018 - Journal of Moral Education 47 (4):519-537.
    ABSTRACTIn this article, we discuss the benefits of Bayesian statistics and how to utilize them in studies of moral education. To demonstrate concrete examples of the applications of Bayesian statistics to studies of moral education, we reanalyzed two data sets previously collected: one small data set collected from a moral educational intervention experiment, and one big data set from a large-scale Defining Issues Test-2 survey. The results suggest that Bayesian analysis of data sets collected from moral (...)
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  46. Natural Language Processing and Semantic Network Visualization for Philosophers.Mark Alfano & Andrew Higgins - forthcoming - In Eugen Fischer & Mark Curtis (eds.), Methodological Advances in Experimental Philosophy. Bloomsbury.
    Progress in philosophy is difficult to achieve because our methods are evidentially and rhetorically weak. In the last two decades, experimental philosophers have begun to employ the methods of the social sciences to address philosophical questions. However, the adequacy of these methods has been called into question by repeated failures of replication. Experimental philosophers need to incorporate more robust methods to achieve a multi-modal perspective. In this chapter, we describe and showcase cutting-edge methods for data-mining and visualization. Big (...) is a useful investigatory tool for moral psychology, and it fits well with the Ramsification method the first author advances in a series of recent papers. The guiding insight of these papers is that we can infer the meaning and structure of concepts from patterns of assertions and inferential associations in natural language. (shrink)
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  47. Philosophy and Theory of Artificial Intelligence 2017.Vincent Müller (ed.) - 2017 - Berlin: Springer.
    This book reports on the results of the third edition of the premier conference in the field of philosophy of artificial intelligence, PT-AI 2017, held on November 4 - 5, 2017 at the University of Leeds, UK. It covers: advanced knowledge on key AI concepts, including complexity, computation, creativity, embodiment, representation and superintelligence; cutting-edge ethical issues, such as the AI impact on human dignity and society, responsibilities and rights of machines, as well as AI threats to humanity and AI safety; (...)
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  48.  5
    Feature Selection Methods for Solving the Reference Class Problem.James Franklin - 2010 - Columbia Law Review Sidebar 110:12-23.
    Probabilistic inference from frequencies, such as "Most Quakers are pacifists; Nixon is a Quaker, so probably Nixon is a pacifist" suffer from the problem that an individual is typically a member of many "reference classes" (such as Quakers, Republicans, Californians, etc) in which the frequency of the target attribute varies. How to choose the best class or combine the information? The article argues that the problem can be solved by the feature selection methods used in contemporary Big Data science: (...)
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  49.  36
    Scale, Anonymity, and Political Akrasia in Aristotle’s Politics 7.4.Joshua Schulz - 2016 - In Travis Dumsday (ed.), The Wisdom of Youth: Essays Inspired by the Early Work of Jacques and Raïssa Maritain. Washington, DC, USA: pp. 295-309.
    This essay articulates and defends Aristotle’s argument in Politics 7.4 that there is a rational limit to the size of the political community. Aristotle argues that size can negatively affect the ability of an organized being to attain its proper end. After examining the metaphysical grounds for this principle in both natural beings and artifacts, we defend Aristotle’s extension of the principle to the polis. He argues that the state is in the relevant sense an organism, one whose primary end (...)
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    Open Data, Open Review and Open Dialogue in Making Social Sciences Plausible.Quan-Hoang Vuong - 2017 - Nature: Scientific Data Updates 2017.
    Nowadays, protecting trust in social sciences also means engaging in open community dialogue, which helps to safeguard robustness and improve efficiency of research methods. The combination of open data, open review and open dialogue may sound simple but implementation in the real world will not be straightforward. However, in view of Begley and Ellis’s (2012) statement that, “the scientific process demands the highest standards of quality, ethics and rigour,” they are worth implementing. More importantly, they are feasible to work (...)
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