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Projet XAI
Projet XAI
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France
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Projet XAI
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France

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Description

The rapid development and the high demand for artificial intelligence (AI) led to prioritize the performance of algorithms over their interpretability ; a lack of interpretability of machine learning techniques poses legal, operational and ethical problems. A new area of research is emerging and focusing on the question of the impenetrability of AI: the interpretable machine learning (ML). A new dynamic in which interpretability could become the new criterion for evaluating models. Our E3 project consists in building explanations of machine learning models, which we consider as "black boxes", in the form of a "toolbox". We focus on the techniques that seem to be the most relevant, namely LIME, SHAP, PDP, ICE, permutation features and shapley value.

Tutors : Giovanni CHIERCHIA, Thibaud VIENNE

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General contact
Projet XAI
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France

Website



Company profile
 
Contacts on the booth
StandContact
Ms Yovana Dentika
yovana.dentika@edu.esiee.fr
Interprétabilité des décisions d'un modèle de machine learning en Python
www.esiee.fr
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_ France
StandContact
Ms Uthamy Thanabalasingam
uthamy.thanabalasingam@edu.esiee.fr
Interprétabilité des décisions d'un modèle de machine learning en Python
www.esiee.fr
_
_ France
StandContact
Ms Marjolaine Claret
marjolaine.claret@edu.esiee.fr
Interprétabilité des décisions d'un modèle de machine learning en Python
www.esiee.fr
_
_ France
StandContact
Ms Morgane BESNIER
morgane.besnier@edu.esiee.fr
FR,EN
Projet XAI
https://join.skype.com/zfzesbSxDB4B
_ France