🇫🇷A Meta-Model for Predicting the Quality of Knowledge Elicitation Sessions

Hussein Jouni, L’Oréal Research & Innovation

Presented at the 10th Annual BayesiaLab Conference on Tuesday, October 25, 2022, at 13:30 (UTC).

Abstract

Capitalizing on expert knowledge can be useful for a company. It can be for transmitting all the know-how on a given field, incorporating technical aspects for decision making, or building causal models for doing prediction. This knowledge can be represented through a Bayesian Network in order to introduce uncertainty on the phenomenon, and, combined with Data, its performance can be improved. Elicitation is done thanks to sessions where expert works together to build models thanks to a facilitator and a modeler. The experts are asked to be available for a given amount of time, which can be large (several days), with a risk that at the end of the sessions, they will not be able to have a satisfying tool. In the context of multi-project management, we propose a tool to assess the probability of success of Elicitation sessions on a given problem. This tool is obtained thanks to the Elicitation of a Bayesian Network (meta-model).

Presentation Video

About the Presenter

Hussein Jouni, Statistical Engineer at L’Oréal Research & Innovation

I studied biomedical engineering at ESIEE PARIS and at the Faculty of Medicine of Paris XII University. After obtaining my degree and my first experience at Danone Nutricia Research, I specialized in clinical data science and clinical research. I’ve been working for L’Oréal (Research and Innovation division) for five years as a Statistical Engineer – Data Scientist.