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

Alexis Bogroff () and Dominique Guegan ()
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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]

Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) from HAL

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.

Keywords: Artificial Intelligence; Bias; Big Data; Ethics; Governance; Interpretability; Regulation; Risk (search for similar items in EconPapers)
Date: 2019-06
New Economics Papers: this item is included in nep-big, nep-cmp and nep-pay
Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-02181597
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Citations: View citations in EconPapers (4)

Published in 2019

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