Despite the popularity of Vision Transformers (ViTs) and eXplainable AI (XAI), only a few explanation methods have been designed specially for ViTs thus far. They mostly use attention weights of the [CLS] token on patch embeddings and often produce unsatisfactory saliency maps. This paper proposes a novel method for explaining ViTs called ViT-CX. It is based on patch embeddings, rather than attentions paid to them, and their causal impacts on the model output. Other characteristics of ViTs such as causal overdetermination are considered in the design of ViT-CX. The empirical results show that ViT-CX produces more meaningful saliency maps and does a better job revealing all important evidence for the predictions than previous methods. The explanation generated by ViT-CX also shows significantly better faithfulness to the model. The codes and appendix are available at https://github.com/vaynexie/CausalX-ViT.

The need to explain the output of a deep neural network classifier is now widely recognized. While previous methods typically explain a single class in the output, we advocate explaining the whole output, which is a probability distribution over multiple classes. A whole-output explanation can help a human user gain an overall understanding of model behaviour instead of only one aspect of it. It can also provide a natural framework where one can examine the evidence used to discriminate between competing classes, and thereby obtain contrastive explanations. In this paper, we propose a contrastive whole-output explanation (CWOX) method for image classification, and evaluate it using quantitative metrics and through human subject studies. The source code of CWOX is available at https://github.com/vaynexie/CWOX.

Recently, neural networks based models have been widely used for recommender systems (RS). Unfortunately, the existing neural network based RS solutions are often treated as black-boxes, which gain little trust and confidence from users. Thus, there is an increasing demand of explainability. Several explainable recommendation methods have been introduced to RS. However, there is a trade-off between explainability and performance among these methods. In this paper, we propose a novel framework, the Tower Bridge Net (TB-Net), using the proposed bidirectional embedding propagation approach to achieve both superior recommendation and explainability performances. Extensive validation on three public datasets shows that the performance of TB-Net dominates the state-of-the-art models. We quantitatively evaluate the explainability by using numerical metrics and experimentally prove that TB-Net achieves a significant improvement on explainability compared with existing methods. More importantly, TB-Net has been deployed and offers explainable recommendation service for the largest bank in China, Industrial and Commercial Bank of China Limited (ICBC). Results on a billion-scale dataset (1.2 billion nodes and edges) from ICBC show that TB-Net can provide both accurate recommendations and semantic explanations, and is very effective and deployable in practice.

We are witnessing a fast development of Artificial Intelligence (AI), but it becomes dramatically challenging to explain AI models in the past decade. “Explanation” has a flexible philosophical concept of “satisfying the subjective curiosity for causal information”, driving a wide spectrum of methods being invented and/or adapted from many aspects and communities, including machine learning, visual analytics, human-computer interaction and so on. Nevertheless, from the view-point of data and knowledge engineering (DKE), a best explaining practice that is cost-effective in terms of extra intelligence acquisition should exploit the causal information and explaining scenarios which is hidden richly in the data itself. In the past several years, there are plenty of works contributing in this line but there is a lack of a clear taxonomy and systematic review of the current effort. To this end, we propose this survey, reviewing and taxonomizing existing efforts from the view-point of DKE, summarizing their contribution, technical essence and comparative characteristics. Specifically, we categorize methods into data-driven methods where explanation comes from the task-related data, and knowledge-aware methods where extraneous knowledge is incorporated. Furthermore, in the light of practice, we provide survey of state-of-art evaluation metrics and deployed explanation applications in industrial practice.

It has been long debated that eXplainable AI (XAI) is an important technology for model and data exploration, validation, and debugging. To deploy XAI into actual systems, an executable and comprehensive evaluation of the quality of generated explanation is highly in demand. In this paper, we briefly summarize the status quo of the quantitative metrics of different properties of XAI including evaluation on faithfulness, localization, sensitivity check, and stability. With an exhaustive experimental study based on them, we conclude that among all the typical methods we compare, no single explanation method dominates others in all metrics. Nonetheless, Gradient-weighted Class Activation Mapping (Grad-CAM) and Randomly Input Sampling for Explanation (RISE) perform fairly well in most of the metrics. We further present a novel utilization of the evaluation results to diagnose the classification bases for models. Hopefully, this valuable work could serve as a guide for future research.

Deep learning has shown powerful performances in many fields, however its black-box nature hinders its further applications. In response, explainable artificial intelligence emerges, aiming to explain the predictions and behaviors of deep learning models. Among many explanation methods, counterfactual explanation has been identified as one of the best methods due to its resemblance to human cognitive process: to deliver an explanation by constructing a contrastive situation so that human may interpret the underlying mechanism by cognitively demonstrating the difference.