Artificial Intelligence, Data, Ethics An Holistic Approach for Risks and Regulation
36 Pages Posted: 15 Jul 2019
Date Written: July 9, 2019
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
JEL Classification: C4, C5, C6, C8, D8, G28, G38, K2
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