Hostname: page-component-78c5997874-94fs2 Total loading time: 0 Render date: 2024-11-10T10:44:09.120Z Has data issue: false hasContentIssue false

Big Data and Us: Human–Data Interactions

Published online by Cambridge University Press:  19 July 2019

Barry C. Smith*
Affiliation:
Institute of Philosophy, School of Advanced Study, University of London, Senate House, Malet Street, London WC1E 7HU, UK. Email: Barry.Smith@sas.ac.uk

Abstract

The growth of continuously generated, large-scale datasets, and new analytics to handle them, has created expectations, in some quarters, that new insights can be generated that will help us address the biggest challenges that face us as a species and therefore can shape future societal outcomes. It is hoped that these new technologies will lead not just to new discoveries but also to new questions and thinking that will deliver significant scientific advances. Perhaps there will be some genuine scientific advances but since many of the challenges that face us reside in the human world and depend upon how humans behave, we need to turn to the humanities and the social sciences as well as the natural sciences and look at the role Big Data could play there in adding to, or shaping, our future. And here, what concerns us is not just the assumptions guiding the new analytic techniques for data mining, data merging, linking and analysis, born out of smarter AI algorithms, it is a more fundamental issue about the constraints and limitations of the kind of data inputs and outputs being appealed to in Big Data systems and whether they are well served to provide an understanding of the human world.

Type
Articles
Copyright
© Academia Europaea 2019 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Miller, H.J. (2010) The data avalanche is here. Shouldn’t we be digging? Journal of Regional Science, 50(1), pp. 181201.CrossRefGoogle Scholar
Kitchen, R. (2014) Big Data, new epistemologies and paradigm shifts. Big Data & Society, 1(April–June), pp. 112.CrossRefGoogle Scholar
Open Data Center Alliance (2012) Big Data Consumer Guide. Available at: http://www.opendatacenteralliance.org/docs/Big_Data_Consumer_Guide_Rev1.0.pdf Google Scholar
Constantine, J. (2012) How big is Facebook’s data? Quoted in R. Kitchen (2014) Big Data, new epistemologies and paradigm shifts. Big Data & Society, 1(April–June), pp. 112.Google Scholar
Darwin, F. and Seward, A.C. (Eds) (1903) More letters of Charles Darwin. A Record of his Work in a Series of Hitherto Unpublished Letters (London: John Murray), Vol. 1, p. 195.Google Scholar
Anderson, C. (2008) The end of theory: The data deluge makes the scientific method obsolete. Wired, 23 June. Available at: http://www.wired.com/science/discoveries/magazine/16-07/pb_theory Google Scholar
Prensky, M. (2009) H. sapiens digital: from digital immigrants and digital natives to digital wisdom. Innovate: Journal of Online Education, 5(3), Article 1. Available at: https://nsuworks.nova.edu/innovate/vol5/iss3/1 Google Scholar
Dyche, J. (2012) Big data ‘Eurekas!’ don’t just happen. Harvard Business Review, 18, Blog. 20 November.Google Scholar
Clark, L. (2013) No questions asked: big data firm maps solutions without human input. Wired UK. URL: http://www.wired.co.uk/news/archive/2013-01/16/ayasdi-big-data-launch Google Scholar
Thanks to Anil Seth for making this point when presenting an earlier version of this paper.Google Scholar
Kitchin in effect, concedes this point: ‘It is one thing to identify patterns; it is another to explain them. This requires social theory and deep contextual knowledge. As such, the pattern is not the endpoint but rather a starting point for additional analysis, which almost certainly is going to require other data sets, see R. Kitchen (2014) Big Data, new epistemologies and paradigm shifts. Big Data & Society, April–June, p. 8, emphasis added.CrossRefGoogle Scholar
See P. Dahlstedt, this issue.Google Scholar
Rumelhart, D., Hinton, G. and Williams, R. (1986) Parallel distributed processing: explorations in the micro-structure of cognition. In: D. Rumelhart and J. McClelland (Eds), Psychological and Biological Models (Cambridge, MA: MIT Press), vol. 2, pp. 318362.Google Scholar
Rumelhart, D. and McClelland, J. (1986) On learning the past tenses of English verbs: implicit rules or parallel distributed processing? In: McClelland, J., Rumelhart, D. and the PDP Research Group (Eds), Parallel Distributed Processing: Explorations in the Microstructure of Cognition (Cambridge, MA: MIT Press).Google Scholar
Plunkett, K. and Marchman, V. (1993) From rote learning to system building: acquiring verb morphology in children and connectionist nets. Cognition, 48(1), pp. 2169. K. Plunkett and V. Marchman (1996) Learning from a connectionist model of the acquisition of the English past tense. Cognition, 61(3), 299–308.CrossRefGoogle ScholarPubMed
Boyd, D. and Crawford, K. (2012) Six provocations for Big Date in a decade. In: Paper presented at Oxford Internet Institute’s Internet Time: Symposium on the Dynamics of the Internet and Society, September 2011.Google Scholar
Ellison, N., Heino, R. and Gibbs, J. (2006) Managing impressions online. Quoted in L. Manovich (2011) Trending: the promises and challenges of Big Data. Debates in the Digital Humanities (edited by Gold, M.K.) (Minneapolis: The University of Minnesota Press) [online].Google Scholar
Byrne, D. (2015) A great curator beats any big company’s algorithm. New Statesman, 1 June.Google Scholar
Ribes, D. and Jackson, S. (2013) Data bite man: The work of sustaining a long-term study. Quoted in R. Kitchen (2014) Big Data, new epistemologies and paradigm shifts. Big Data & Society, April–June, pp. 112.Google Scholar
Ravazi, B., Eshani, P., Kamrava, M., Pekelis, L., Nangia, V., Chafe, C. and Parvizi, J.. (2015) The brain stethoscope: a device that turns brain activity into sound. Epilepsy and Behaviour, 46, pp. 5354.Google Scholar
Spence, C. (2017) Gastrophysics: the New Science of Eating (London: Penguin Books).Google Scholar
See Spence, C., Auvray, M. and Smith, B. (2014) How not to confuse taste and flavour in perception and its modalities. In: Stokes, D., Matthen, M., and Briggs, S. (Eds), Perception and its Modalities (Oxford: Oxford University Press).Google Scholar
Mouritsen, O. and Styrbæk, K. (2017) Mouthfeel: How Texture Makes Taste (Arts and Traditions of the Table: Perspectives on Culinary History) (New York: Columbia University Press).CrossRefGoogle Scholar
Wang, Y., Ma, X., Luo, Q. and Qu, H. (2016) Data edibilization: representing data with food. Proceedings of the 2016 CHI Conference 2016, San Jose, CA – dl.acm.orgCrossRefGoogle Scholar
Garfinkel, S.N., Seth, A., Barett, A., Susuki, K. and Critchley, H. (2015) Knowing your own heart. Biological Psychology, 104, January, pp. 6574.CrossRefGoogle ScholarPubMed
Kandesamy, N., Garfinkel, S., Page, L., Hardy, B., Critchley, H., Gurnell, M. and Coates, J. (2016) Interoceptive accuracy predicts survival on a London trading floor. Scientific Reports, 6, article number: 32986.Google Scholar
For the duality of our sense of smell see Rozen, P. (1982) Taste–smell confusions and the duality of the olfactory sense. Perception & Psychophysics, 31, pp. 397401. For recent work on smell disorders, see T. Hummel, B. Landis and K. Huttenbrink (2011) Smell and taste disorders, current topics in otorhinolaryngology. Head and Neck Surgery, 10, pp. 115.CrossRefGoogle Scholar