Towards relatable explainable AI

A novel contrastive explanation method for neural network classifiers is proposed and applied in the challenging realm of audio prediction. The method draws inspiration for cognitive psychology: The explanations are based on extracting cues, creating counterfactual examples, and generating contrast cues.

The difficulty to understand the decisions of complex neural networks is a major limiting factor for their adoption in practice. While a huge amount of techniques have been proposed, many of them provide rather technical explanations which are not easily understood by laymen.

A good example are saliency maps. In audio prediction tasks one typically highlights the saliency maps on audiograms or spectrograms. However, such saliency maps offer only limited transparency because spectrograms are too technical to be interpreted by non-domain experts. Another drawback is that the most important regions for a decision might coincide among all possible classes. For instance, the saliency maps for an emotion prediction model trained on face images might always highlight the eyes and the mouth of the face. Hence, saliency maps are insufficient to assess why a certain emotion was predicted by the model. The authors of [Zha22R] (best-paper award at CHI 2022) argue further that the way in which an AI system makes its decision should be human-like in order to earn people’s trust. They draw inspiration from theories in cognitive psychology by designing their architecture after the perceptual process [Car78P], which states that people select, organize, and interpret information to make decisions: 1) First, a subset of the sensory information is selected. 2) The selected regions get organized into meaningful cues, e.g. ears, mouth, and nose for a face. 3) Finally, the low-level cues get interpreted towards high-level concepts, e.g. the characteristics of ears, mouth, and nose are used to distinguish humans from animals.

In the proposed architecture, the first step of the perceptual process relates to saliency maps where the high saliency regions represent the selected subset of the sensory information. The cues are domain specific and predefined. Depending on the context they can either be computed directly from the input or learned from annotated examples. Most theories assume that the brain generates or remembers counterfactual examples and compares the current perception against them on the cues in order to attribute the perception to the high level concepts. The authors propose to use generative adversarial networks and style transfer to generate examples from all classes that are otherwise similar to the input. The system uses the differences on the cues to provide contrastive cues as an additional layer of explanation. The final classification is made from an embedding of the input that is obtained from the original model and the contrastive cues, which makes the decision process more relatable. The entire architecture is depicted below. The system provides the saliency maps, the cues and the contrastive cues to explain the decision.

The paper concludes with extensive empirical studies on a voice emotion prediction task. They show that the model does not only provide better explanations in think-alout and controlled user studies, it also outperforms the vanilla convolutional neural network model in accuracy. If you would like to try it, there is a nice demo and some short video presentations on the authors lab page.


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