Explanation of Deep Learning Models via Logic Rules Enhanced by Embeddings Analysis, and Probabilistic Models

by Contreras Ordoñez Victor Hugo, Michael I. Schumacher and Davide Calvaresi

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 uncertainty 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) integration of textual data into the explanation pipeline through embedding analysis.

• URL: https://publications.hevs.ch/index.php/publications/show/3145
• DOI: https://doi.org/10.1007/978-3-031-70074-3_9

@INPROCEEDINGS{contreras2024explanation,
     author = {Contreras, Victor H. and Schumacher, Michael and Calvaresi, Davide},
     keywords = {Deep Learning, Heterogeneous data processing, Preference modeling, rule extraction, Uncertainty reasoning, XAI},
     month = aug,
     title = {Explanation of Deep Learning Models via Logic Rules Enhanced by Embeddings Analysis, and Probabilistic Models},booktitle = {Post-proceedings of the 6th International Workshop on EXplainable and TRAnsparent AI and Multi-Agent Systems},
     year = {2024},
     pages={155--183},s
     organization={Springer},
     doi={10.1007/978-3-031-70074-3_9},
     isbn={978-3-031-70074-3}
}