Transparent Data Mining for Big and Small Data pp 147-167

Part of the Studies in Big Data book series (SBD, volume 32) | Cite as

Beyond the EULA: Improving Consent for Data Mining

Chapter

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.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Centre for Research in ComputingThe Open UniversityMilton KeynesUK
  2. 2.School of Computer ScienceUniversity of St AndrewsSt AndrewsUK

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