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

Author

Listed:
  • Alexis Bogroff

    () (University Paris 1 Panthéon-Sorbonne)

  • Dominique Guégan

    () (University Paris 1 Panthéon-Sorbonne; labEx ReFi France; University Ca’ Foscari Venice)

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 Guégan, 2019. "Artificial Intelligence, Data, Ethics. An Holistic Approach for Risks and Regulation," Working Papers 2019: 19, Department of Economics, University of Venice "Ca' Foscari".
  • Handle: RePEc:ven:wpaper:2019:19
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    References listed on IDEAS

    as
    1. Dominique Guegan & Peter Addo & Bertrand Hassani, 2018. "Credit Risk Analysis Using Machine and Deep Learning Models," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01835164, HAL.
    2. 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".
    3. Peter Addo & Dominique Guegan & Bertrand Hassani, 2018. "Credit Risk Analysis using Machine and Deep Learning models," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01719983, HAL.
    4. Renault, Thomas, 2017. "Intraday online investor sentiment and return patterns in the U.S. stock market," Journal of Banking & Finance, Elsevier, vol. 84(C), pages 25-40.
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    6. Calvano, Emilio & Calzolari, Giacomo & Denicolò, Vincenzo & Pastorello, Sergio, 2018. "Artificial intelligence, algorithmic pricing and collusion," CEPR Discussion Papers 13405, C.E.P.R. Discussion Papers.
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    9. J Banasik & J Crook, 2005. "Credit scoring, augmentation and lean models," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(9), pages 1072-1081, September.
    10. Peter Martey Addo & Dominique Guégan & Bertrand Hassani, 2018. "Credit Risk Analysis using Machine and Deep learning models," Documents de travail du Centre d'Economie de la Sorbonne 18003, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    11. Timo Klein, 2018. "Autonomous Algorithmic Collusion: Q-Learning Under Sequantial Pricing," Tinbergen Institute Discussion Papers 18-056/VII, Tinbergen Institute, revised 01 Nov 2019.
    12. repec:dau:papers:123456789/353 is not listed on IDEAS
    13. Kevin A. Clarke, 2005. "The Phantom Menace: Omitted Variable Bias in Econometric Research," Conflict Management and Peace Science, Peace Science Society (International), vol. 22(4), pages 341-352, September.
    14. Peter Martey Addo & Dominique Guegan & Bertrand Hassani, 2018. "Credit Risk Analysis Using Machine and Deep Learning Models," Risks, MDPI, Open Access Journal, vol. 6(2), pages 1-20, April.
    Full references (including those not matched with items on IDEAS)

    Citations

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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.

    More about this item

    Keywords

    Artificial Intelligence; Bias; Big Data; Ethics; Governance; Interpretability; Regulation; Risk;

    JEL classification:

    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs
    • D8 - Microeconomics - - Information, Knowledge, and Uncertainty
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation
    • G38 - Financial Economics - - Corporate Finance and Governance - - - Government Policy and Regulation
    • K2 - Law and Economics - - Regulation and Business Law

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