Name
Abstract | ||
|---|---|---|
ABSTRACTDeep 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. In this tutorial, we will introduce the cognitive concept and characteristics of counterfactual explanation, its computational form, mainstream methods, and various adaptation in terms of different explanation settings. In addition, we will demonstrate several typical use cases of counterfactual explanations in popular research areas. Finally, in light of practice, we outline the potential applications of counterfactual explanations like data augmentation or conversation system. We hope this tutorial can help the participants get an overview sense of counterfactual explanations. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1145/3447548.3470797 | Knowledge Discovery and Data Mining |
Keywords | DocType | Citations |
Explainable Artificial Intelligence, Interpretable Machine Learning, Counterfactual Explanation, Computational Cognition, Deep Learning | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cong Wang | 1 | 2 | 0.74 |
| Xiao-Hui Li | 2 | 0 | 0.68 |
| Haocheng Han | 3 | 0 | 0.34 |
| Shendi Wang | 4 | 0 | 0.68 |
| Luning Wang | 5 | 0 | 0.34 |
| Caleb Chen Cao | 6 | 292 | 12.15 |
| Lei Chen | 7 | 6239 | 395.84 |