Paper ID: 2409.16693

CaBRNet, an open-source library for developing and evaluating Case-Based Reasoning Models

Romain Xu-Darme (LSL), Aymeric Varasse (LSL), Alban Grastien (LSL), Julien Girard (LSL), Zakaria Chihani (LSL)

In the field of explainable AI, a vibrant effort is dedicated to the design of self-explainable models, as a more principled alternative to post-hoc methods that attempt to explain the decisions after a model opaquely makes them. However, this productive line of research suffers from common downsides: lack of reproducibility, unfeasible comparison, diverging standards. In this paper, we propose CaBRNet, an open-source, modular, backward-compatible framework for Case-Based Reasoning Networks: this https URL.

Submitted: Sep 25, 2024