CLIMAX: An exploration of Classifier-Based Contrastive Explanations
Praharsh Nanavati Ranjitha Prasad
Explainable AI is an evolving area that deals with understanding the decision
making of machine learning models so that these models are more transparent,
accountable, and understandable for humans. In particular, post-hoc
model-agnostic interpretable AI techniques explain the decisions of a black-box
ML model for a single instance locally, without the knowledge of the intrinsic
nature of the ML model. Despite their simplicity and capability in providing
valuable insights, existing approaches fail to deliver consistent and reliable
explanations. Moreover, in the context of black-box classifiers, existing
approaches justify the predicted class, but these methods do not ensure that
the explanation scores strongly differ as compared to those of another class.
In this work we propose a novel post-hoc model agnostic XAI technique that
provides contrastive explanations justifying the classification of a black box
classifier along with a reasoning as to why another class was not predicted.
Our method, which we refer to as CLIMAX which is short for Contrastive
Label-aware Influence-based Model Agnostic XAI, is based on local classifiers .
In order to ensure model fidelity of the explainer, we require the
perturbations to be such that it leads to a class-balanced surrogate dataset.
Towards this, we employ a label-aware surrogate data generation method based on
random oversampling and Gaussian Mixture Model sampling. Further, we propose
influence subsampling in order to retaining effective samples and hence ensure
sample complexity. We show that we achieve better consistency as compared to
baselines such as LIME, BayLIME, and SLIME. We also depict results on textual
and image based datasets, where we generate contrastive explanations for any
black-box classification model where one is able to only query the class
probabilities for an instance of interest.