Data governance: Organizing data for trustworthy Artificial Intelligence

Janssen, Marijn, Brous, Paul, Estevez, Elsa, Soares Barbosa, Luís and Janowski, Tomasz, (2020). Data governance: Organizing data for trustworthy Artificial Intelligence. Government Information Quarterly, 37(3), n/a-n/a

Document type:

  • Sub-type Journal article
    Author Janssen, Marijn
    Brous, Paul
    Estevez, Elsa
    Soares Barbosa, Luís
    Janowski, Tomasz
    Title Data governance: Organizing data for trustworthy Artificial Intelligence
    Appearing in Government Information Quarterly   Check publisher's open access policy
    Volume 37
    Issue No. 3
    Publication Date 2020-06
    Place of Publication Amsterdam
    Publisher Elsevier
    Start page n/a
    End page n/a
    Language eng
    Abstract The rise of Big, Open and Linked Data (BOLD) enables Big Data Algorithmic Systems (BDAS) which are often based on machine learning, neural networks and other forms of Artificial Intelligence (AI). As such systems are increasingly requested to make decisions that are consequential to individuals, communities and society at large, their failures cannot be tolerated, and they are subject to stringent regulatory and ethical requirements. However, they all rely on data which is not only big, open and linked but varied, dynamic and streamed at high speeds in real-time. Managing such data is challenging. To overcome such challenges and utilize opportunities for BDAS, organizations are increasingly developing advanced data governance capabilities. This paper reviews challenges and approaches to data governance for such systems, and proposes a framework for data governance for trustworthy BDAS. The framework promotes the stewardship of data, processes and algorithms, the controlled opening of data and algorithms to enable external scrutiny, trusted information sharing within and between organizations, risk-based governance, system-level controls, and data control through shared ownership and self-sovereign identities. The framework is based on 13 design principles and is proposed incrementally, for a single organization and multiple networked organizations.
    Keyword big data
    data governance
    algorithmic governance
    information sharing
    trusted frameworks
    research line governance
    research line technology
    Copyright Holder Elsevier
    Copyright Year 2020
    Copyright type All rights reserved
    ISSN 0740-624X
    DOI 10.1016/j.giq.2020.101493
  • Versions
    Version Filter Type
  • Citation counts
    Google Scholar Search Google Scholar
    Access Statistics: 572 Abstract Views  -  Detailed Statistics
    Created: Thu, 11 Feb 2021, 03:47:36 JST by Mario Peixoto on behalf of UNU EGOV