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Alternative personal data governance models

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  • Greshake Tzovaras, Bastian
  • Ball, Mad Price

Abstract

The not-so-secret ingredient that underlies all successful Artificial Intelligence / Machine Learning (AI/ML) methods is training data. There would be no facial recognition, no targeted advertisements and no self-driving cars if it was not for large enough data sets with which those algorithms have been trained to perform their tasks. Given how central these data sets are, important ethics questions arise: How is data collection performed? And how do we govern its' use? This chapter – part of a forthcoming book – looks at why new data governance strategies are needed; investigates the relation of different data governance models to historic consent approaches; and compares different implementations of personal data exchange models.

Suggested Citation

  • Greshake Tzovaras, Bastian & Ball, Mad Price, 2019. "Alternative personal data governance models," MetaArXiv bthj7, Center for Open Science.
    • Handle: RePEc:osf:metaar:bthj7
    DOI: 10.31219/osf.io/bthj7
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    References listed on IDEAS

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    1. Jay R Corrigan & Saleem Alhabash & Matthew Rousu & Sean B Cash, 2018. "How much is social media worth? Estimating the value of Facebook by paying users to stop using it," PLOS ONE, Public Library of Science, vol. 13(12), pages 1-11, December.
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