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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2510.11164 |
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| _version_ | 1866909963994005504 |
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| author | Vascotto, Ilaria Rodriguez, Alex Bonaita, Alessandro Bortolussi, Luca |
| author_facet | Vascotto, Ilaria Rodriguez, Alex Bonaita, Alessandro Bortolussi, Luca |
| contents | The use of Artificial Intelligence (AI) models in real-world and high-risk applications has intensified the discussion about their trustworthiness and ethical usage, from both a technical and a legislative perspective. The field of eXplainable Artificial Intelligence (XAI) addresses this challenge by proposing explanations that bring to light the decision-making processes of complex black-box models. Despite being an essential property, the robustness of explanations is often an overlooked aspect during development: only robust explanation methods can increase the trust in the system as a whole. This paper investigates the role of robustness through the usage of a feature importance aggregation derived from multiple models ($k$-nearest neighbours, random forest and neural networks). Preliminary results showcase the potential in increasing the trustworthiness of the application, while leveraging multiple model's predictive power. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_11164 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Beyond single-model XAI: aggregating multi-model explanations for enhanced trustworthiness Vascotto, Ilaria Rodriguez, Alex Bonaita, Alessandro Bortolussi, Luca Machine Learning The use of Artificial Intelligence (AI) models in real-world and high-risk applications has intensified the discussion about their trustworthiness and ethical usage, from both a technical and a legislative perspective. The field of eXplainable Artificial Intelligence (XAI) addresses this challenge by proposing explanations that bring to light the decision-making processes of complex black-box models. Despite being an essential property, the robustness of explanations is often an overlooked aspect during development: only robust explanation methods can increase the trust in the system as a whole. This paper investigates the role of robustness through the usage of a feature importance aggregation derived from multiple models ($k$-nearest neighbours, random forest and neural networks). Preliminary results showcase the potential in increasing the trustworthiness of the application, while leveraging multiple model's predictive power. |
| title | Beyond single-model XAI: aggregating multi-model explanations for enhanced trustworthiness |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2510.11164 |