| Title | The Analysis and Development of an XAI Process on Feature Contribution Explanation |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Huang, Jun, Wang, Zerui, Li, Ding, Liu, Yan |
| Conference Name | 2022 IEEE International Conference on Big Data (Big Data) |
| Date Published | dec |
| Keywords | Analytical models, Big Data, big data security metrics, codes, Data models, Deep Learning, explainable AI, Feature Importance, machine learning, Measurement, pubcrawl, resilience, Resiliency, Scalability, Systematics, Taxonomy, XAI Process |
| Abstract | Explainable Artificial Intelligence (XAI) research focuses on effective explanation techniques to understand and build AI models with trust, reliability, safety, and fairness. Feature importance explanation summarizes feature contributions for end-users to make model decisions. However, XAI methods may produce varied summaries that lead to further analysis to evaluate the consistency across multiple XAI methods on the same model and data set. This paper defines metrics to measure the consistency of feature contribution explanation summaries under feature importance order and saliency map. Driven by these consistency metrics, we develop an XAI process oriented on the XAI criterion of feature importance, which performs a systematical selection of XAI techniques and evaluation of explanation consistency. We demonstrate the process development involving twelve XAI methods on three topics, including a search ranking system, code vulnerability detection and image classification. Our contribution is a practical and systematic process with defined consistency metrics to produce rigorous feature contribution explanations. |
| DOI | 10.1109/BigData55660.2022.10020313 |
| Citation Key | huang_analysis_2022 |
The Analysis and Development of an XAI Process on Feature Contribution Explanation