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Erschienen in:
Introduction Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

2. Open Data, Big Data, and Just Data

verfasst von : Jeffrey Alan Johnson

Erschienen in: Toward Information Justice

Verlag: Springer International Publishing

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Abstract

This chapter examines two cases in which data presents questions of justice. Many argue as a philosophical principle that data sources should be available as widely as possible, the principle at the heart of the open data movement. But as I argue in that chapter, open data can just as easily lead to injustice: Like programming, “Injustice in, injustice out” ought to be a principle of data. Social privilege can color the data that is opened and create serious inequalities in who can access and use ostensibly open data. Open data can also establish standards that exclude knowledge that is not part of the data system. In the second case, I consider what big data means for higher education. After discussing some recent examples, I identify two types of ethical challenges in the increasingly common use of predictive analytics at universities: challenges related to the direct consequences of the systems and those rooted in the ideology of scientism that inspire them. Both the open data and big data cases prove quite problematic if the aim is just data.

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Vorheriges Kapitel Introduction
Nächstes Kapitel The Construction of Data
Fußnoten
1
Evelyn Cruz, e-mail correspondence, March 29–31, 2013.
 
2
Jane Doe (pseudonym), personal communication, March 20, 2013.
 
3
For the purpose of this chapter, I will use the terms data mining to refer to the general task of identifying relationships in large datasets without a priori theoretical bases and predictive analytics to refer to the mathematical and computational techniques used in the practice of data mining. Readers are advised, however, that this distinction is introduced in the paper for clarity and is not based on more broadly accepted convention in the field; the two terms are often used interchangeably in the broader literature.
 
4
It is, of course, advisable that the model be tested against a new dataset, often a portion of the original dataset reserved for that purpose. With some predictive analytic techniques this is necessary, as it is possible for the model to over-fit the data. Neural nets, for example, will inevitably produce a model that exactly matches the dataset on which the net is trained if allowed sufficient iterations and hidden layers, but once the model begins to incorporate stochastic variation, it will show increasing error when applied to data on which the model was not trained (Two Crows Corporation 2005).
 
5
In this section, I use “consequences” and related terms strictly in a non-technical sense, referring to moral conditions that arise consequent to the implementation of a data mining process. At this point, I take no position on the relative merits of formally consequentialist or deontological ethical theories in evaluating those circumstances, though it will become clear to readers familiar with the distinction through the examples that follow that I believe that both kinds of ethical theory at least raise questions that data miners should address.
 
6
See, for example, Haack’s (1993) critique of Quine’s naturalism for a technical treatment. A useful non-technical perspective on scientism can be found in Kitcher (2012).
 
7
Delavari and colleagues do not identify the university in which their study is conducted or its location, thus whether there are legal constraints that would prevent such a policy is unknown. Even if there are such constraints, however, such constraints are external to the criticism being made here; the finding and the failure of the authors to address the question of its spuriousness suggest that such conclusions are likely in areas in which the law presents no such constraint to designing an intervention around a spurious relationship that would harm the subjects of the model.
 
Literatur
Arendt, H. (1973). The origins of totalitarianism. New York: Harcourt Brace Jovanovich.
Ayesha, S., Mustafa, T., Sattar, A., & Khan, M. (2010). Data mining model for higher education system. European Journal of Scientific Research, 43(1), 24–29.
Baepler, P., & Murdoch, C. J. (2010). Academic analytics and data mining in higher education. International Journal for the Scholarship of Teaching and Learning, 4(2), 17.CrossRef
Baez, B. (2009). The politics of inquiry: Education research and the “culture of science”. Albany: State University of New York Press.
Baker, R. S. J. D. (2010). Data mining for education. In B. McGaw, P. Peterson, & E. Baker (Eds.), International encyclopedia of education (3rd ed.). Oxford: Elsevier.
Baradwaj, B. K., & Pal, S. (2011). Mining educational data to analyze students’ performance. International Journal of Advanced Computer Science and Applications, 2(6), 63–69.
Danna, A., & Gandy, O. (2002). All that glitters is not gold: Digging beneath the surface of data mining. Journal of Business Ethics, 40(4), 373–386.CrossRef
Delavari, N., Beizadeh, M. R., & Phon-Amnuaisuk, S. (2005). Application of enhanced analysis model for data mining processes in higher educational system. In 2005 6th international conference on information technology based higher education and training, F4B–1–F4B–6. doi:https://​doi.​org/​10.​1109/​ITHET.​2005.​1560303.
Delavari, N., Phon-Amnuaisuk, S., & Beizadeh, M. R. (2008). Data mining application in higher learning institutions. Informatics in Education, 7(1), 31–54.
Dewey, J. (1954). The public and its problems. Athens: Swallow Press.
Dworkin, G. (1995). Autonomy. In R. E. Goodin & P. Pettit, A. (Eds.), Companion to contemporary political philosophy (pp. 359–365). Cambridge, MA: Blackwell.
Flathman, R. E. (1996). Liberal versus civic, republican, democratic, and other vocational educations: Liberalism and institutionalized education. Political Theory, 24(1), 4–32.CrossRef
Foucault, M. (1995). Discipline and punish: The birth of the prison (2nd ed.). New York: Vintage Books.
Gutmann, A. (1999). Democratic education. Princeton: Princeton University Press.CrossRef
Haack, S. (1993). Evidence and inquiry: Towards reconstruction in epistemology. Oxford: Blackwell.
Habermas, J. (1990). The philosophical discourse of modernity. Cambridge, MA: MIT Press.
King, G. (1986). How not to lie with statistics: Avoiding common mistakes in quantitative political science. American Journal of Political Science, 30(3), 666–687.CrossRef
Kumar, V., & Chadha, A. (2011). An empirical study of the applications of data mining techniques in higher education. International Journal of Advanced Computer Science and Applications, 2(3), 80–84.CrossRef
Lehrer, T. (1965). In W. Von Braun (Ed.), On That was the week that was. Reprise/Warner Bros Records.
Nissenbaum, H. (2010). Privacy in context: Technology, policy, and the integrity of social life. Stanford: Stanford Law Books.
Peters, R. S. (2010). What is an educational process? In R. S. Peters (Ed.), The concept of education (pp. 1–16). Oxford: Routledge.
Pollack, P. H. I. (2012). The essentials of political analysis (4th ed.). Washington, DC: CQ Press.
Scott, J. C. (1998). Seeing like a state: How certain schemes to improve the human condition have failed. New Haven: Yale University Press.
Thomas, E., & Galambos, N. (2004). What satisfies students? Mining student-opinion data with regression and decision tree analysis. Research in Higher Education, 45(3), 251–269.CrossRef
Zhang, Y., Oussena, S., Clark, T., & Kim, H. (2010). Use data mining to improve student retention in higher education: A case study. In J. Filippe & J. Cordiero (Eds.), Proceedings of the 12th international conference on enterprise information systems (Vol. 1, pp. 190–197). Funchal: SciTePress.
Metadaten
Titel
Open Data, Big Data, and Just Data
verfasst von
Jeffrey Alan Johnson
Copyright-Jahr
2018
Buch
Toward Information Justice

Print ISBN: 978-3-319-70892-8

Electronic ISBN: 978-3-319-70894-2

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-70894-2_2