The role of explainability in creating trustworthy artificial intelligence for health care: A comprehensive survey of the terminology, design choices, and evaluation strategies
The role of explainability in creating trustworthy artificial intelligence for health care: A comprehensive survey of the terminology, design choices, and evaluation strategies
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DOI：
10.1016/j.jbi.2020.103655
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发表时间：
2021-01-01
影响因子：
4.5
通讯作者：
Rijnbeek, Peter R.
中科院分区：
文献类型：
--
作者：
Markus, Aniek F.;Kors, Jan A.;Rijnbeek, Peter R.
人工智能(AI)在改善人们的健康和福祉方面具有巨大的潜力，但在临床实践中的采用仍然有限。缺乏透明度被认为是实施的主要障碍之一，因为临床医生应该相信人工智能系统是可以信任的。可解释的人工智能有可能克服这个问题，并可能是迈向值得信赖的人工智能的一步。在本文中，我们回顾了最近的文献，以期为研究人员和实践者提供指导，为医疗保健领域设计可解释的人工智能系统，并有助于可解释人工智能领域的形式化。我们认为，要求可解释性的原因决定了应该解释什么，因为这决定了可解释性属性(即可解释性和保真性)的相对重要性。在此基础上，我们提出了一个框架来指导可解释人工智能方法类别之间的选择(可解释建模和事后解释；基于模型、基于属性或基于示例的解释；全局解释和局部解释)。此外，我们发现，对于客观标准化评估很重要的量化评估指标，在某些属性(例如清晰度)和解释类型(例如基于实例的方法)方面仍然缺乏。我们的结论是，可解释的建模可以有助于可信的人工智能，但可解释性的好处仍然需要在实践中得到证明，并且可能需要补充措施来在医疗保健中创建可信的人工智能(例如，报告数据质量、执行广泛的(外部)验证和监管)。
Artificial intelligence (AI) has huge potential to improve the health and well-being of people, but adoption in clinical practice is still limited. Lack of transparency is identified as one of the main barriers to implementation, as clinicians should be confident the AI system can be trusted. Explainable AI has the potential to overcome this issue and can be a step towards trustworthy AI. In this paper we review the recent literature to provide guidance to researchers and practitioners on the design of explainable AI systems for the health-care domain and contribute to formalization of the field of explainable AI. We argue the reason to demand explainability determines what should be explained as this determines the relative importance of the properties of explainability (i.e. interpretability and fidelity). Based on this, we propose a framework to guide the choice between classes of explainable AI methods (explainable modelling versus post-hoc explanation; model-based, attribution-based, or example-based explanations; global and local explanations). Furthermore, we find that quantitative evaluation metrics, which are important for objective standardized evaluation, are still lacking for some properties (e.g. clarity) and types of explanations (e.g. example-based methods). We conclude that explainable modelling can contribute to trustworthy AI, but the benefits of explainability still need to be proven in practice and complementary measures might be needed to create trustworthy AI in health care (e.g. reporting data quality, performing extensive (external) validation, and regulation).