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

Alexis Bogroff () and Dominique Guégan ()
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Alexis Bogroff: Université Paris1 Panthéon-Sorbonne
Dominique Guégan: Université Paris1 Panthéon-Sorbonne, Centre d'Economie de la Sorbonne, LabEx ReFi and Ca' Foscari University of Venezia, https://cv.archives-ouvertes.fr/dominique-guegan

Documents de travail du Centre d'Economie de la Sorbonne from Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne

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)
JEL-codes: C4 C5 C6 C8 D8 G28 G38 K2 (search for similar items in EconPapers)
Pages: 35 pages
Date: 2019-06
New Economics Papers: this item is included in nep-big, nep-cmp, nep-hme, nep-ore and nep-pay
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:mse:cesdoc:19012

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