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Artificial Intelligence, Data, Ethics: An Holistic Approach for Risks and Regulation

Author

Listed:
  • Alexis Bogroff

    (UP1 - Université Paris 1 Panthéon-Sorbonne)

  • Dominique Guegan

    (UP1 - Université Paris 1 Panthéon-Sorbonne, CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, Labex ReFi - UP1 - Université Paris 1 Panthéon-Sorbonne, University of Ca’ Foscari [Venice, Italy])

Abstract

An extensive list of risks relative to big data frameworks and their use through models of artificial intelligence is provided along with measurements and implementable solutions. Bias, interpretability and ethics are studied in depth, with several interpretations from the point of view of developers, companies and regulators. Reflexions suggest that fragmented frameworks increase the risks of models misspecification, opacity and bias in the result; Domain experts and statisticians need to be involved in the whole process as the business objective must drive each decision from the data extraction step to the final activatable prediction. We propose an holistic and original approach to take into account the risks encountered all along the implementation of systems using artificial intelligence from the choice of the data and the selection of the algorithm, to the decision making.

Suggested Citation

  • Alexis Bogroff & Dominique Guegan, 2019. "Artificial Intelligence, Data, Ethics: An Holistic Approach for Risks and Regulation," Post-Print halshs-02181597, HAL.
    • Handle: RePEc:hal:journl:halshs-02181597
    Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-02181597
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    References listed on IDEAS

    as
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    3. Peter Martey Addo & Dominique Guegan & Bertrand Hassani, 2018. "Credit Risk Analysis using Machine and Deep learning models," Working Papers 2018:08, Department of Economics, University of Venice "Ca' Foscari".
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    Cited by:

    1. Dominique Guegan, 2020. "A Note on the Interpretability of Machine Learning Algorithms," Post-Print halshs-02900929, HAL.
    2. Dominique Guégan, 2020. "A Note on the Interpretability of Machine Learning Algorithms," Working Papers 2020:20, Department of Economics, University of Venice "Ca' Foscari".
    3. Dominique Guegan, 2020. "A Note on the Interpretability of Machine Learning Algorithms," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-02900929, HAL.
    4. Dominique Guégan, 2020. "A Note on the Interpretability of Machine Learning Algorithms," Documents de travail du Centre d'Economie de la Sorbonne 20012, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.

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    More about this item

    Keywords

    Artificial Intelligence; Bias; Big Data; Ethics; Governance; Interpretability; Regulation; Risk;
    All these keywords.

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