Explainability matters: The effect of liability rules on the healthcare sector
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866911168151420928 |
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| author | Wei, Jiawen Verona, Elena Bertolini, Andrea Mengaldo, Gianmarco |
| author_facet | Wei, Jiawen Verona, Elena Bertolini, Andrea Mengaldo, Gianmarco |
| contents | Explainability, the capability of an artificial intelligence system (AIS) to explain its outcomes in a manner that is comprehensible to human beings at an acceptable level, has been deemed essential for critical sectors, such as healthcare. Is it really the case? In this perspective, we consider two extreme cases, ``Oracle'' (without explainability) versus ``AI Colleague'' (with explainability) for a thorough analysis. We discuss how the level of automation and explainability of AIS can affect the determination of liability among the medical practitioner/facility and manufacturer of AIS. We argue that explainability plays a crucial role in setting a responsibility framework in healthcare, from a legal standpoint, to shape the behavior of all involved parties and mitigate the risk of potential defensive medicine practices. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_17334 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Explainability matters: The effect of liability rules on the healthcare sector Wei, Jiawen Verona, Elena Bertolini, Andrea Mengaldo, Gianmarco Computers and Society Artificial Intelligence Computational Engineering, Finance, and Science Machine Learning Explainability, the capability of an artificial intelligence system (AIS) to explain its outcomes in a manner that is comprehensible to human beings at an acceptable level, has been deemed essential for critical sectors, such as healthcare. Is it really the case? In this perspective, we consider two extreme cases, ``Oracle'' (without explainability) versus ``AI Colleague'' (with explainability) for a thorough analysis. We discuss how the level of automation and explainability of AIS can affect the determination of liability among the medical practitioner/facility and manufacturer of AIS. We argue that explainability plays a crucial role in setting a responsibility framework in healthcare, from a legal standpoint, to shape the behavior of all involved parties and mitigate the risk of potential defensive medicine practices. |
| title | Explainability matters: The effect of liability rules on the healthcare sector |
| topic | Computers and Society Artificial Intelligence Computational Engineering, Finance, and Science Machine Learning |
| url | https://arxiv.org/abs/2509.17334 |