Explainable AI: Definition and attributes of a good explanation for health AI
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866916408486526976 |
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| author | Kyrimi, Evangelia McLachlan, Scott Wohlgemut, Jared M Perkins, Zane B Lagnado, David A. Marsh, William Group, the ExAIDSS Expert |
| author_facet | Kyrimi, Evangelia McLachlan, Scott Wohlgemut, Jared M Perkins, Zane B Lagnado, David A. Marsh, William Group, the ExAIDSS Expert |
| contents | Proposals of artificial intelligence (AI) solutions based on increasingly complex and accurate predictive models are becoming ubiquitous across many disciplines. As the complexity of these models grows, transparency and users' understanding often diminish. This suggests that accurate prediction alone is insufficient for making an AI-based solution truly useful. In the development of healthcare systems, this introduces new issues related to accountability and safety. Understanding how and why an AI system makes a recommendation may require complex explanations of its inner workings and reasoning processes. Although research on explainable AI (XAI) has significantly increased in recent years and there is high demand for XAI in medicine, defining what constitutes a good explanation remains ad hoc, and providing adequate explanations continues to be challenging. To fully realize the potential of AI, it is critical to address two fundamental questions about explanations for safety-critical AI applications, such as health-AI: (1) What is an explanation in health-AI? and (2) What are the attributes of a good explanation in health-AI? In this study, we examined published literature and gathered expert opinions through a two-round Delphi study. The research outputs include (1) a definition of what constitutes an explanation in health-AI and (2) a comprehensive list of attributes that characterize a good explanation in health-AI. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2409_15338 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Explainable AI: Definition and attributes of a good explanation for health AI Kyrimi, Evangelia McLachlan, Scott Wohlgemut, Jared M Perkins, Zane B Lagnado, David A. Marsh, William Group, the ExAIDSS Expert Computers and Society Artificial Intelligence Proposals of artificial intelligence (AI) solutions based on increasingly complex and accurate predictive models are becoming ubiquitous across many disciplines. As the complexity of these models grows, transparency and users' understanding often diminish. This suggests that accurate prediction alone is insufficient for making an AI-based solution truly useful. In the development of healthcare systems, this introduces new issues related to accountability and safety. Understanding how and why an AI system makes a recommendation may require complex explanations of its inner workings and reasoning processes. Although research on explainable AI (XAI) has significantly increased in recent years and there is high demand for XAI in medicine, defining what constitutes a good explanation remains ad hoc, and providing adequate explanations continues to be challenging. To fully realize the potential of AI, it is critical to address two fundamental questions about explanations for safety-critical AI applications, such as health-AI: (1) What is an explanation in health-AI? and (2) What are the attributes of a good explanation in health-AI? In this study, we examined published literature and gathered expert opinions through a two-round Delphi study. The research outputs include (1) a definition of what constitutes an explanation in health-AI and (2) a comprehensive list of attributes that characterize a good explanation in health-AI. |
| title | Explainable AI: Definition and attributes of a good explanation for health AI |
| topic | Computers and Society Artificial Intelligence |
| url | https://arxiv.org/abs/2409.15338 |