人工智能中的“解释”:来自社会科学的洞见(上)

Explanation in Artificial Intelligence:Insights from the Social Sciences

随着研究人员和从业者寻求为其算法提供更多透明度,可解释人工智能(Explainable Artificial Intelligence)有一场新近的复苏。这项研究的大部分聚焦于向人类观察者明确地解释决策或行动。观察人类如何相互解释可以作为人工智能解释的一个有用起点,这应该没有争议。诚然,公正地说,大多数关于可解释人工智能的工作仅利用研究人员对什么构成“好”解释的直觉。哲学、心理学和认知科学领域存在大量关于人们如何定义、生成、选择、评估和呈现解释的有价值的研究,这些研究认为人们在解释过程中运用了某些认知偏见和社会期望。文章认为,可解释人工智能领域可以建立在这些现有研究的基础上,并回顾了研究这些主题的哲学、认知心理学/科学和社会心理学的相关论文,由此获取了一些重要的发现,并讨论了如何将这些发现融入可解释人工智能的相关工作中。

There has been a recent resurgence in the area of explainable artificial intelligence as researchers and practitioners seek to provide more transparency to their algorithms.Much of this research is focused on explicitly explaining decisions or actions to a human observer,and it should not be controversial to say that looking at how humans explain to each other can serve as a useful starting point for explanation in artificial intelligence.However,it is fair to say that most work in explainable artificial intelligence uses only the researchers'intuition of what constitutes a ‘good’explanation.There exist vast and valuable bodies of research in philosophy,psychology,and cognitive science of how people define,generate,select,evaluate,and present explanations,which argues that people employ certain cognitive biases and social expectations to the explanation process.This paper argues that the field of explainable artificial intelligence can build on this existing research,and reviews relevant papers from philosophy,cognitive psychology/science,and social psychology,which study these topics.It draws out some important findings,and discusses ways that these can be infused with work on explainable artificial intelligence.