Abstraction, validation , and generalization for explainable artificial intelligence
Abstraction, validation , and generalization for explainable artificial intelligence
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可解释人工智能的抽象、验证和泛化
DOI：
10.1002/ail2.37
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Shafto, Patrick
中科院分区：
文献类型：
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作者：
Yang, Scott Cheng‐Hsin;Folke, Tomas;Shafto, Patrick
神经网络架构在不断扩大的任务范围上实现了超人的性能。为了有效和安全地部署这些系统，他们的决策必须能够被广泛的利益相关者所理解。已经提出了解释人工智能（AI）的方法来应对这一挑战，但缺乏理论阻碍了系统抽象的发展，这是积累知识所必需的。我们提出贝叶斯教学作为一个框架，通过整合机器学习和人类学习来统一可解释AI（XAI）。贝叶斯教学将解释形式化为解释者改变被解释者信念的交流行为。这种形式化将广泛的XAI方法分解为四个部分：（a）目标推理，（B）解释，（c）被解释者模型，以及（d）解释者模型。贝叶斯教学分解XAI方法所提供的抽象阐明了它们之间的不变性。XAI系统的分解可以进行模块化验证，因为列出的前三个组件中的每个组件都可以半独立地进行测试。这种分解还通过重组来自不同XAI系统的组件来促进泛化，这有助于生成新的变体。只要每个组件都经过验证，就不需要逐一评估这些新变体，从而使开发时间呈指数级减少。最后，通过明确解释的目标，贝叶斯教学帮助开发人员评估XAI系统在多大程度上适合其预期的真实的世界用例。因此，贝叶斯教学提供了一个理论框架，鼓励系统的，科学的调查XAI。
Neural network architectures are achieving superhuman performance on an expanding range of tasks. To effectively and safely deploy these systems, their decision‐making must be understandable to a wide range of stakeholders. Methods to explain artificial intelligence (AI) have been proposed to answer this challenge, but a lack of theory impedes the development of systematic abstractions, which are necessary for cumulative knowledge gains. We propose Bayesian Teaching as a framework for unifying explainable AI (XAI) by integrating machine learning and human learning. Bayesian Teaching formalizes explanation as a communication act of an explainer to shift the beliefs of an explainee. This formalization decomposes a wide range of XAI methods into four components: (a) the target inference, (b) the explanation, (c) the explainee model, and (d) the explainer model. The abstraction afforded by Bayesian Teaching to decompose XAI methods elucidates the invariances among them. The decomposition of XAI systems enables modular validation, as each of the first three components listed can be tested semi‐independently. This decomposition also promotes generalization through recombination of components from different XAI systems, which facilitates the generation of novel variants. These new variants need not be evaluated one by one provided that each component has been validated, leading to an exponential decrease in development time. Finally, by making the goal of explanation explicit, Bayesian Teaching helps developers to assess how suitable an XAI system is for its intended real‐world use case. Thus, Bayesian Teaching provides a theoretical framework that encourages systematic, scientific investigation of XAI.