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Luciano Floridi (2012). Big Data and Their Epistemological Challenge.

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  1.  9
    Big Data Analytics, Infectious Diseases and Associated Ethical Impacts.Chiara Garattini, Jade Raffle, Dewi N. Aisyah, Felicity Sartain & Zisis Kozlakidis - forthcoming - Philosophy and Technology:1-17.
    The exponential accumulation, processing and accrual of big data in healthcare are only possible through an equally rapidly evolving field of big data analytics. The latter offers the capacity to rationalize, understand and use big data to serve many different purposes, from improved services modelling to prediction of treatment outcomes, to greater patient and disease stratification. In the area of infectious diseases, the application of big data analytics has introduced a number of changes in the information accumulation models. These are (...)
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  2.  3
    Algo-Rhythms and the Beat of the Legal Drum.Ugo Pagallo - forthcoming - Philosophy and Technology:1-18.
    The paper focuses on concerns and legal challenges brought on by the use of algorithms. A particular class of algorithms that augment or replace analysis and decision-making by humans, i.e. data analytics and machine learning, is under scrutiny. Taking into account Balkin’s work on “the laws of an algorithmic society”, attention is drawn to obligations of transparency, matters of due process, and accountability. This US-centric analysis on drawbacks and loopholes of current legal systems is complemented with the analysis of norms (...)
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  3.  4
    Making Data Valuable: Political, Economic, and Conceptual Bases of Big Data.Anna Lauren Hoffmann - 2018 - Philosophy and Technology 31 (2):209-212.
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  4.  37
    Simplified Models: A Different Perspective on Models as Mediators.C. D. McCoy & Michela Massimi - 2018 - European Journal for Philosophy of Science 8 (1):99-123.
    We introduce a novel point of view on the “models as mediators” framework in order to emphasize certain important epistemological questions about models in science which have so far been little investigated. To illustrate how this perspective can help answer these kinds of questions, we explore the use of simplified models in high energy physics research beyond the Standard Model. We show in detail how the construction of simplified models is grounded in the need to mitigate pressing epistemic problems concerning (...)
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  5.  35
    From Individual to Group Privacy in Big Data Analytics.Brent Mittelstadt - 2017 - Philosophy and Technology 30 (4):475-494.
    Mature information societies are characterised by mass production of data that provide insight into human behaviour. Analytics has arisen as a practice to make sense of the data trails generated through interactions with networked devices, platforms and organisations. Persistent knowledge describing the behaviours and characteristics of people can be constructed over time, linking individuals into groups or classes of interest to the platform. Analytics allows for a new type of algorithmically assembled group to be formed that does not necessarily align (...)
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  6. The Ethics of Big Data: Current and Foreseeable Issues in Biomedical Contexts.Brent Mittelstadt & Luciano Floridi - 2016 - Science and Engineering Ethics 22 (2):303-341.
    The capacity to collect and analyse data is growing exponentially. Referred to as ‘Big Data’, this scientific, social and technological trend has helped create destabilising amounts of information, which can challenge accepted social and ethical norms. Big Data remains a fuzzy idea, emerging across social, scientific, and business contexts sometimes seemingly related only by the gigantic size of the datasets being considered. As is often the case with the cutting edge of scientific and technological progress, understanding of the ethical implications (...)
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  7.  83
    The Causal Nature of Modeling with Big Data.Wolfgang Pietsch - 2016 - Philosophy and Technology 29 (2):137-171.
    I argue for the causal character of modeling in data-intensive science, contrary to widespread claims that big data is only concerned with the search for correlations. After discussing the concept of data-intensive science and introducing two examples as illustration, several algorithms are examined. It is shown how they are able to identify causal relevance on the basis of eliminative induction and a related difference-making account of causation. I then situate data-intensive modeling within a broader framework of an epistemology of scientific (...)
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  8.  54
    Information Quality.Luciano Floridi - 2013 - Philosophy and Technology 26 (1):1-6.