Industrialiser
l’Intelligence Artificielle

A guide of the most promising XAI libraries

Using Machine Learning to solve a problem is good, understanding how it does is better. Indeed, many AI-based systems are characterized by their obscure nature. When seeking an explanation, looking to understand how a given result was produced, exposing the system’s architecture or its parameters alone is rarely enough. Explaining that a CNN recognized a dog by detailing the Neural Network weights is, to say the least, obscure. Even for models deemed as glass-box such as decision trees, a proper explanation is never obvious.

Les difficultés de l'industrialisation de l'IA

Pendant longtemps, l’objectif principal d’un Data Scientist a été de trouver la meilleure recette algorithmique pour répondre à un problème métier donné. Pour faciliter cette phase de prototypage, de nombreux outils ont vu le jour comme les librairies open-source et les plateformes de Data Science ; ces dernières allant même jusqu’à proposer une expérience no-code.