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Design Challenges of Trustworthy Artificial Intelligence Learning Systems

  • Matthias R. BrustEmail author
  • Pascal Bouvry
  • Grégoire Danoy
  • El-Ghazil Talbi
Conference paper
  • 202 Downloads
Part of the Communications in Computer and Information Science book series (CCIS, volume 1178)

Abstract

In the near future, more than two thirds of the world’s population is expected to be living in cities. In this interconnected world, data collection from various sensors is eased up and unavoidable. Handling the right data is an important factor for decision making and improving services. While at the same time keeping the right level of privacy for end users is crucial. This position paper discusses the necessary trade-off between privacy needs and data handling for the improvement of services. Pseudo-anonymization techniques have shown their limits and local computation and aggregation of data seems the way to go. To illustrate the opportunity, the case for a novel generation of clustering algorithms is made that implements a privacy by design approach. Preliminary results of such a clustering algorithm use case show that our approach exhibits a high degree of elasticity.

Keywords

Artificial Intelligence Trustworthiness Smart cities Data-driven economies 

Notes

Acknowledgment

This work has been partially funded by the joint research programme University of Luxembourg/SnT-ILNAS on Digital Trust for Smart-ICT.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Matthias R. Brust
    • 1
    Email author
  • Pascal Bouvry
    • 1
    • 2
  • Grégoire Danoy
    • 1
    • 2
  • El-Ghazil Talbi
    • 3
  1. 1.Interdisciplinary Centre for Security Reliability and Trust (SnT)Luxembourg CityLuxembourg
  2. 2.Faculty of Science, Technology and Medicine (FSTM)University of LuxembourgLuxembourg CityLuxembourg
  3. 3.Polytech’Lille, University Lillie - InriaLilleFrance

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