Explanation of Deep Learning Models via Logic Rules Enhanced by Embeddings Analysis, and Probabilistic Models
| Type of publication: | Inproceedings |
| Citation: | |
| Booktitle: | Post-proceedings of the 6th International Workshop on EXplainable and TRAnsparent AI and Multi-Agent Systems |
| Year: | 2024 |
| Month: | August |
| Abstract: | Deep Learning (DL) models are increasingly dealing with heterogeneous data (i.e., a mix of structured and unstructured data), calling for adequate eXplainable Artificial Intelligence (XAI) methods. Nevertheless, only some of the existing techniques consider the uncer- tainty inherent to the data. To this end, this study proposes a pipeline to explain heterogeneous data-based DL models by combining embed- ding analysis, rule extraction methods, and probabilistic models. The proposed pipeline has been tested using synthetic data (multi-individual food items tracking). This study has achieved (i) inference enhancement through probabilistic and evidential reasoning, (ii) generation of logical explanations based on extracted rules and predictions, and (iii) integra- tion of textual data into the explanation pipeline through embedding analysis. |
| Keywords: | Deep Learning, Heterogeneous data processing, Preference modeling, rule extraction, Uncertainty reasoning, XAI |
| Authors | |
| Added by: | [] |
| Total mark: | 0 |
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