Why Should I Choose You? AutoXAI: A Framework for Selecting and Tuning eXplainable AI Solutions

1 code implementation • 6 Oct 2022 • Robin Cugny, Julien Aligon, Max Chevalier, Geoffrey Roman Jimenez, Olivier Teste

In this paper, we propose AutoXAI, a framework that recommends the best XAI solution and its hyperparameters according to specific XAI evaluation metrics while considering the user's context (dataset, ML model, XAI needs and constraints).

AutoML Explainable artificial intelligence +2

Coalitional strategies for efficient individual prediction explanation

1 code implementation • 1 Apr 2021 • Gabriel Ferrettini, Elodie Escriva, Julien Aligon, Jean-Baptiste Excoffier, Chantal Soulé-Dupuy

As Machine Learning (ML) is now widely applied in many domains, in both research and industry, an understanding of what is happening inside the black box is becoming a growing demand, especially by non-experts of these models.