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Data objects for knowing

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Abstract

Although true in some aspects, the suggested characterization of today’s science as a dichotomy between traditional science and data-driven science misses some of the nuance, complexity, and possibility that exists between the two positions. Part of the problem is the claim that Data Science works without theories. There are many theories behind the data that are used in science. However, for data science, the only theories that matter are those in mathematics, statistics, and computer science. In this conceptual paper, we add two other philosophy of science tenets, experiments and data, to the discussion to create a more nuanced view of how data science uses theories. Following Ihde’s concept of technoscience and the incessant quest for more precision, magnification, and resolution, we argue that technology-driven science created a need for more technology-driven science, culminating in data science. Further, we adapt Hacking and Galison’s views on physics to argue that data science is also an experimental science, which uses data objects in experiments. Drawing from Heelan (The Journal of Philosophy 85:515–524, 1988), we called these objects “data-objects-for-knowing”. Finally, we conclude that data science is a science to study artificially created phenomena—a science to study the data manipulated by the equations and operations of AI. It disregards the connections between data and the real world that were carefully built by the theories from other sciences. In the experiments of data science, data are the world itself. The knowledge created by data science is purposely disconnected from any theory from other sciences; it is a knowledge for the sake of itself.

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Notes

  1. Slota et al. define prospecting as “the work of rendering data, knowledge, expertise, and practices of worldly domains available or amenable to engagement with data scientific method and epistemology, including mapping available data sources and tools, surveying potential organizational connections, and reasoning about future resources. Prospecting precedes data analysis or visualization, and is constituted by the activities of discovering disordered or inaccessible data resources, thereafter to be ordered and rendered available for data scientific work.” (2020, p.1).

References

  • Ackermann RJ (2014) Data, instruments, and theory: a dialectical approach to understanding science. Princeton University Press, Princeton Legacy Library

    Google Scholar 

  • Agarwal R, Dhar V (2014) Big data, data science, and analytics: The opportunity and challenge for is research. Inf Syst Res 25(3):443–448. https://doi.org/10.1287/isre.2014.0546

    Article  Google Scholar 

  • Anderson C (2008) The End of theory: The data deluge makes the scientific method obsolete. Wired 16(07). https://www.wired.com/2008/06/pb-theory/. Accessed 30 Jan 2021

  • Appenzeller T (2017) The scientists’ apprentice. Science 357(6346):16–17

    MathSciNet  Google Scholar 

  • Bohannon J (2017) The cyberscientist. Science 357(6346):18–21

    MathSciNet  Google Scholar 

  • Canali S (2016) Big data, epistemology and causality: knowledge in and knowledge out in EXPOsOMICS. Big Data Soc 3(2):2053951716669530

    Google Scholar 

  • Cao L (2017) Data science: a comprehensive overview. ACM Comput Surv 50(3):2–42. https://doi.org/10.1145/3076253

    Google Scholar 

  • Dhar V (2013) Data science and prediction. Commun ACM 56(12):64–73

    Google Scholar 

  • Dourish P (2017) The stuff of bits: an essay on the materialities of information. The MIT Press, Cambridge, MA

    Google Scholar 

  • Feyerabend P (1970) Consolations for the specialist. Criticism and the Growth of Knowledge 4:197–230

    Google Scholar 

  • Feyerabend P (1993) Against method. Verso, London

    Google Scholar 

  • Fonseca F, Marcinkowski M, Davis C (2019) Cyber-human systems of thought and understanding. J Assoc Inf Sci Technolo 70(4):402–411

    Google Scholar 

  • Gadamer H-G, Fantel H (1975) The problem of historical consciousness. Graduate Fac Philos J 5(1):8–52

    Google Scholar 

  • Galison P (1987) How experiments end. University of Chicago Press, Chicago

    Google Scholar 

  • Galison P (1997) Image and logic: a material culture of microphysics. University of Chicago Press, Image and Logic

    Google Scholar 

  • Galison P (2011) Computer simulations and the trading zone. In: Gramelsberger G (ed) From science to computational science. Diaphanes, Diaphanes, Zürich, pp 118–157

    Google Scholar 

  • Galison P, Stump D (1996) The disunity of science: boundaries, contexts, and power. Stanford University Press, Writing science

    Google Scholar 

  • Glazebrook T (2000) Heidegger’s philosophy of science. Perspectives in continental philosophy. Fordham University Press, New York

    Google Scholar 

  • Hacking I (1982) Experimentation and scientific realism. Philosophical Topics 13(1):71–87

    Google Scholar 

  • Hacking I (1988) On the stability of the laboratory sciences. J Philos 85(10):507–514

    Google Scholar 

  • Hacking I (1990) Book review: how experiments end. J Philos 87(2):103–106

    Google Scholar 

  • Heelan P (1988) Experiment and theory: constitution and reality. J Philos 85(10):515–524

    Google Scholar 

  • Heelan P (1989a) After experiment: realism and research. Am Philos Q 26(4):297–308

    Google Scholar 

  • Heelan P (1989b) Space-perception and the philosophy of science. University of California Press, California

    Google Scholar 

  • Heidegger M (1977) The question concerning technology, and other essays. Garland Pub, New York

  • Hey T, Hey AJG, Tansley S, Tolle KM (2009) The fourth paradigm: data-intensive scientific discovery. Microsoft Research, Redmond, WA

  • Horvitz E (2017) AI, people, and society. Science 357(6346):7

    Google Scholar 

  • Humphreys P (2009) The philosophical novelty of computer simulation methods. Synthese 169(3):615–626

    MathSciNet  Google Scholar 

  • Ihde D (1991) Instrumental realism: the interface between philosophy of science and philosophy of technology. Indiana University Press, Bloomington, IN

    Google Scholar 

  • Ihde D (2016) Husserl’s missing technologies. perspectives in continental philosophy. Fordham University Press, New York

    Google Scholar 

  • Kitchin R (2014) Big data, new epistemologies and paradigm shifts. Big Data Soc 1(1):1–12

    Google Scholar 

  • Kuhn TS (1970) The structure of scientific revolutions. University of Chicago Press, Chicago

    Google Scholar 

  • Latour B (1987) Science in action: how to follow scientists and engineers through society. Harvard University Press, Cambridge, MA

    Google Scholar 

  • Mayer-Schonberger V, Cukier K (2013) Big data: a revolution that will transform how we live, work, and think. Houghton Mifflin Harcourt, Boston

    Google Scholar 

  • Naur P (1974) Concise survey of computer methods. Petrocelli Books.

  • Norvig P (2012) Colorless green ideas learn furiously: Chomsky and the two cultures of statistical learning. Significance 9(4):30–33

    Google Scholar 

  • Pietsch W (2015) Aspects of theory-ladenness in data-intensive science. Philosophy of Science 82(5):905–916

    Google Scholar 

  • Popper K (2002) The logic of scientific discovery. Classics series. Routledge, UK

    MATH  Google Scholar 

  • Radder H (2003) The philosophy of scientific experimentation. University of Pittsburgh Press, Pittsburgh

    Google Scholar 

  • Ratti E (2015) Big data biology: between eliminative inferences and exploratory experiments. Philosophy of Science 82(2):198–218

    Google Scholar 

  • Ribes D, Hoffman AS, Slota SC, Bowker GC (2019) The logic of domains. Soc Stud Sci 49(3):281–309

    Google Scholar 

  • Ruse M (2005) Theory. In: Honderich T (ed) The Oxford companion to philosophy, 2nd edn. Oxford University Press, Oxford

    Google Scholar 

  • Slota SC, Hoffman AS, Ribes D, Bowker GC (2020) Prospecting (in) the data sciences. Big Data Soc 7(1):2053951720906849

    Google Scholar 

  • Susser D (2016) Ihde’s missing sciences: postphenomenology, big data, and the human sciences. Techné: Res Philos Technol 20(2):137–152

    Google Scholar 

  • Tuana N (2013) Embedding philosophers in the practices of science: bringing humanities to the sciences. Synthese 190(11):1955–1973

    Google Scholar 

  • Waters CK (2007) The nature and context of exploratory experimentation: an introduction to three case studies of exploratory research. Hist Philos Life Sci 29(3):275–284

    Google Scholar 

  • Winther RG (2016) The Structure of Scientific Theories. In: Zalta EN (ed) The stanford encyclopedia of philosophy. Metaphysics Research Lab, Stanford University, California

    Google Scholar 

  • Wittgenstein L, Pears D, Russell B, McGuinness B (2001) Tractatus Logico-philosophicus. Routledge, Routledge classics

    Google Scholar 

Download references

Acknowledgments

We gratefully acknowledge the Rock Ethics Institute at The Pennsylvania State University for supporting this work under the Faculty Fellows program.

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Funded by Rock Ethics Institute Faculty Fellowship at The Pennsylvania State University.

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  1. College of Information Sciences and Technology, Penn State University, University Park, PA, USA

    Fred Fonseca

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  1. Fred Fonseca
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Correspondence to Fred Fonseca.

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Fonseca, F. Data objects for knowing. AI & Soc 37, 195–204 (2022). https://doi.org/10.1007/s00146-021-01150-y

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