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Modelling GDPR-Compliant Explanations for Trustworthy AI

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Electronic Government and the Information Systems Perspective (EGOVIS 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12394))

Abstract

Through the General Data Protection Regulation (GDPR), the European Union has set out its vision for Automated Decision-Making (ADM) and AI, which must be reliable and human-centred. In particular we are interested on the Right to Explanation, that requires industry to produce explanations of ADM. The High-Level Expert Group on Artificial Intelligence (AI-HLEG), set up to support the implementation of this vision, has produced guidelines discussing the types of explanations that are appropriate for user-centred (interactive) Explanatory Tools. In this paper we propose our version of Explanatory Narratives (EN), based on user-centred concepts drawn from ISO 9241, as a model for user-centred explanations aligned with the GDPR and the AI-HLEG guidelines. Through the use of ENs we convert the problem of generating explanations for ADM into the identification of an appropriate path over an Explanatory Space, allowing explainees to interactively explore it and produce the explanation best suited to their needs. To this end we list suitable exploration heuristics, we study the properties and structure of explanations, and discuss the proposed model identifying its weaknesses and strengths.

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Notes

  1. 1.

    These ontologies do not have necessarily to be explicit, formal or complete.

  2. 2.

    It is not excluded that the original purposes might change during the explanatory process.

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Author information

Authors and Affiliations

  1. DISI, University of Bologna, Bologna, Italy

    Francesco Sovrano & Fabio Vitali

  2. CIRSFID, University of Bologna, Bologna, Italy

    Monica Palmirani

Authors
  1. Francesco Sovrano
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  2. Fabio Vitali
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  3. Monica Palmirani
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Corresponding author

Correspondence to Francesco Sovrano .

Editor information

Editors and Affiliations

  1. Department of Information Systems, Corvinus University of Budapest, Budapest, Hungary

    Andrea Kő

  2. National Research Council, Florence, Italy

    Enrico Francesconi

  3. Johannes Kepler University of Linz, Linz, Oberösterreich, Austria

    Gabriele Kotsis

  4. Vienna University of Technology, Vienna, Wien, Austria

    A Min Tjoa

  5. Johannes Kepler University of Linz, Linz, Oberösterreich, Austria

    Ismail Khalil

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Sovrano, F., Vitali, F., Palmirani, M. (2020). Modelling GDPR-Compliant Explanations for Trustworthy AI. In: Kő, A., Francesconi, E., Kotsis, G., Tjoa, A., Khalil, I. (eds) Electronic Government and the Information Systems Perspective. EGOVIS 2020. Lecture Notes in Computer Science(), vol 12394. Springer, Cham. https://doi.org/10.1007/978-3-030-58957-8_16

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  • DOI: https://doi.org/10.1007/978-3-030-58957-8_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-58956-1

  • Online ISBN: 978-3-030-58957-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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