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

Alexis Bogroff () and Dominique Guégan ()
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Alexis Bogroff: University Paris 1 Panthéon-Sorbonne
Dominique Guégan: University Paris 1 Panthéon-Sorbonne; labEx ReFi France; Ca' Foscari University of Venice

No 2019: 19, Working Papers from Department of Economics, University of Venice "Ca' Foscari"

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: 36 pages
Date: 2019
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ecm, nep-law, nep-ore and nep-pay
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:ven:wpaper:2019:19

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