Reviving Purpose Limitation and Data Minimisation in Personalisation, Profiling and Decision-Making Systems

Authors

  • Michele Finck Max Planck Institute for Innovation and Competition
  • Asia J. Biega Max Planck Institute for Security and Privacy

DOI:

https://doi.org/10.26116/techreg.2021.004

Keywords:

GDPR, data minimisation, purpose limitation, computer science, law, profiling, recommender algorithms, personalisation

Abstract

This paper determines whether the two core data protection principles of data minimi- sation and purpose limitation can be meaningfully implemented in data-driven systems. While contemporary data processing practices appear to stand at odds with these prin- ciples, we demonstrate that systems could technically use much less data than they currently do. This observation is a starting point for our detailed techno-legal analysis uncovering obstacles that stand in the way of meaningful implementation and compliance as well as exemplifying unexpected trade-offs which emerge where data protection law is applied in practice. Our analysis seeks to inform debates about the impact of data protec- tion on the development of artificial intelligence in the European Union, offering practical action points for data controllers, regulators, and researchers.

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Author Biographies

Michele Finck, Max Planck Institute for Innovation and Competition

Michèle Finck is Professor of Law and Artificial Intelligence, University of Tübingen. This research was carried out while I was a Senior Research Fellow at the Max Planck Institute of Innovation and Competition, where I remain an Affiliated Fellow.

Asia J. Biega, Max Planck Institute for Security and Privacy

Asia Biega is a tenure-track faculty member and head of the Responsible Computing group, Max Planck Institute for Security and Privacy

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Published

2021-08-18

How to Cite

Finck, M., & Biega, A. J. (2021). Reviving Purpose Limitation and Data Minimisation in Personalisation, Profiling and Decision-Making Systems. Technology and Regulation, 2021, 44–61. https://doi.org/10.26116/techreg.2021.004

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Section

Articles