Beyond Trivial Counterfactual Explanations with Diverse Valuable Explanations
Pau Rodriguez, Massimo Caccia, Alexandre Lacoste, Lee Zamparo, Issam, Laradji, Laurent Charlin, David Vazquez

TL;DR
This paper introduces DiVE, a method that generates diverse, non-trivial counterfactual explanations for machine learning models, improving interpretability by uncovering multiple valuable insights beyond trivial attribute changes.
Contribution
DiVE learns disentangled perturbations with diversity constraints to produce meaningful, non-trivial counterfactual explanations, addressing limitations of existing methods.
Findings
DiVE outperforms previous methods in generating valuable explanations.
The approach effectively prevents trivial counterfactuals.
Experiments on CelebA and Synbols validate the method's effectiveness.
Abstract
Explainability for machine learning models has gained considerable attention within the research community given the importance of deploying more reliable machine-learning systems. In computer vision applications, generative counterfactual methods indicate how to perturb a model's input to change its prediction, providing details about the model's decision-making. Current methods tend to generate trivial counterfactuals about a model's decisions, as they often suggest to exaggerate or remove the presence of the attribute being classified. For the machine learning practitioner, these types of counterfactuals offer little value, since they provide no new information about undesired model or data biases. In this work, we identify the problem of trivial counterfactual generation and we propose DiVE to alleviate it. DiVE learns a perturbation in a disentangled latent space that is…
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Taxonomy
TopicsExplainable Artificial Intelligence (XAI) · Generative Adversarial Networks and Image Synthesis · Machine Learning in Healthcare
MethodsCounterfactuals Explanations
