| Title | XAI Evaluation: Evaluating Black-Box Model Explanations for Prediction |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Zhang, Yuyi, Xu, Feiran, Zou, Jingying, Petrosian, Ovanes L., Krinkin, Kirill V. |
| Conference Name | 2021 II International Conference on Neural Networks and Neurotechnologies (NeuroNT) |
| Date Published | June 2021 |
| Publisher | IEEE |
| ISBN Number | 978-1-6654-4534-4 |
| Keywords | Biological neural networks, black-box model explanations, boosting, Data models, ensemble models, linear regression, Neural Network, Prediction algorithms, Predictive models, pubcrawl, reliability, resilience, Resiliency, Scalability, xai, XAI evaluation |
| Abstract | The results of evaluating explanations of the black-box model for prediction are presented. The XAI evaluation is realized through the different principles and characteristics between black-box model explanations and XAI labels. In the field of high-dimensional prediction, the black-box model represented by neural network and ensemble models can predict complex data sets more accurately than traditional linear regression and white-box models such as the decision tree model. However, an unexplainable characteristic not only hinders developers from debugging but also causes users mistrust. In the XAI field dedicated to ``opening'' the black box model, effective evaluation methods are still being developed. Within the established XAI evaluation framework (MDMC) in this paper, explanation methods for the prediction can be effectively tested, and the identified explanation method with relatively higher quality can improve the accuracy, transparency, and reliability of prediction. |
| URL | https://ieeexplore.ieee.org/document/9472817 |
| DOI | 10.1109/NeuroNT53022.2021.9472817 |
| Citation Key | zhang_xai_2021 |
XAI Evaluation: Evaluating Black-Box Model Explanations for Prediction