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Beyond the EULA: Improving consent for data mining

Hutton, Luke and Henderson, Tristan (2017). Beyond the EULA: Improving consent for data mining. In: Cerquitelli, Tania; Quercia, Daniele and Pasquale, Frank eds. Transparent Data Mining for Big and Small Data. Studies in Big Data (11). Springer, pp. 147–167.

DOI (Digital Object Identifier) Link: https://doi.org/10.1007/978-3-319-54024-5_7
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Abstract

Companies and academic researchers may collect, process, and distribute large quantities of personal data without the explicit knowledge or consent of the individuals to whom the data pertains. Existing forms of consent often fail to be appropriately readable and ethical oversight of data mining may not be sufficient. This raises the question of whether existing consent instruments are sufficient, logistically feasible, or even necessary, for data mining. In this chapter, we review the data collection and mining landscape, including commercial and academic activities, and the relevant data protection concerns, to determine the types of consent instruments used. Using three case studies, we use the new paradigm of human-data interaction to examine whether these existing approaches are appropriate. We then introduce an approach to consent that has been empirically demonstrated to improve on the state of the art and deliver meaningful consent. Finally, we propose some best practices for data collectors to ensure their data mining activities do not violate the expectations of the people to whom the data relate.

Item Type: Book Section
Copyright Holders: 2017 Springer
ISBN: 3-319-54024-6, 978-3-319-54024-5
Project Funding Details:
Funded Project NameProject IDFunding Body
Monetize Me? Privacy and the Quantified Self in the Digital EconomyEP/L021285/1EPSRC (Engineering and Physical Sciences Research Council)
Keywords: consent; big data; ethics; data mining
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Computing and Communications
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Item ID: 47928
Depositing User: Luke Hutton
Date Deposited: 19 May 2017 10:45
Last Modified: 19 May 2017 10:45
URI: http://oro.open.ac.uk/id/eprint/47928
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