Generative Counterfactuals for Neural Networks via Attribute-Informed Perturbation
This work addresses the need for interpretable AI in high-stakes applications by providing a method to generate counterfactuals for raw data, though it is incremental as it builds on existing counterfactual and generative model approaches.
The paper tackles the challenge of generating counterfactual explanations for raw data like text and images in neural networks, which is difficult due to high dimensionality and lack of semantic features, by proposing an Attribute-Informed Perturbation framework that uses generative models to produce high-quality counterfactuals efficiently, as demonstrated in experiments on real-world datasets.
With the wide use of deep neural networks (DNN), model interpretability has become a critical concern, since explainable decisions are preferred in high-stake scenarios. Current interpretation techniques mainly focus on the feature attribution perspective, which are limited in indicating why and how particular explanations are related to the prediction. To this end, an intriguing class of explanations, named counterfactuals, has been developed to further explore the "what-if" circumstances for interpretation, and enables the reasoning capability on black-box models. However, generating counterfactuals for raw data instances (i.e., text and image) is still in the early stage due to its challenges on high data dimensionality and unsemantic raw features. In this paper, we design a framework to generate counterfactuals specifically for raw data instances with the proposed Attribute-Informed Perturbation (AIP). By utilizing generative models conditioned with different attributes, counterfactuals with desired labels can be obtained effectively and efficiently. Instead of directly modifying instances in the data space, we iteratively optimize the constructed attribute-informed latent space, where features are more robust and semantic. Experimental results on real-world texts and images demonstrate the effectiveness, sample quality as well as efficiency of our designed framework, and show the superiority over other alternatives. Besides, we also introduce some practical applications based on our framework, indicating its potential beyond the model interpretability aspect.