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- handle: 10670/1.oq10ey
- halshs: halshs-02181597
info:eu-repo/semantics/OpenAccess
Mots-clés
Artificial Intelligence Bias Big Data Ethics Governance Interpretability Regulation Risk C - Mathematical and Quantitative Methods/C.C4 - Econometric and Statistical Methods: Special Topics C - Mathematical and Quantitative Methods/C.C5 - Econometric Modeling C - Mathematical and Quantitative Methods/C.C6 - Mathematical Methods • Programming Models • Mathematical and Simulation Modeling C - Mathematical and Quantitative Methods/C.C8 - Data Collection and Data Estimation Methodology • Computer Programs D - Microeconomics/D.D8 - Information, Knowledge, and Uncertainty G - Financial Economics/G.G2 - Financial Institutions and Services/G.G2.G28 - Government Policy and Regulation G - Financial Economics/G.G3 - Corporate Finance and Governance/G.G3.G38 - Government Policy and Regulation K - Law and Economics/K.K2 - Regulation and Business LawSujets proches
Intellectronics AI (Artificial intelligence) Artificial thinking Electronic brains Intelligent machines Intelligence, Artificial Machine intelligence Thinking, Artificial IACiter ce document
Alexis Bogroff et al., « Artificial Intelligence, Data, Ethics: An Holistic Approach for Risks and Regulation », HAL-SHS : droit et gestion, ID : 10670/1.oq10ey
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Résumé
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.