| Autor: |
Alimonda N., Guidotto L., Malandri L., Mercorio F., Mezzanzanica M., Tosi G. |
| Přispěvatelé: |
Alimonda, N, Guidotto, L, Malandri, L, Mercorio, F, Mezzanzanica, M, Tosi, G |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
2022 IEEE International Conference on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE). |
| DOI: |
10.1109/metroxraine54828.2022.9967673 |
| Popis: |
The growing number of machine learning-based Cyber-Physical Systems (CPSs) and their ability to adapt and to learn is gaining research interest in several biomedical applications. The use of learning capabilities allows CPSs to interact and analyse their environment, learn from patterns, and perform highly complex prediction tasks. However, while on the one side the use of machine learning acts as a flywheel to the diffusion of those systems, on the other side exposes them to the problem of transparency and interpretability that affect any machine-learning-based systems. This, in critical fields like medicine, is just as important as models' performances, in order to understand their behaviour, errors, and to garner user trust. In this paper we investigate the role of state-of-the-art explainable AI techniques in the field of cyber-physical systems. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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