可解释的AI：小心管理庇护所的囚犯或：我如何学会停止担忧并热爱社会和行为科学
Explainable AI: Beware of Inmates Running the Asylum Or: How I Learnt to Stop Worrying and Love the Social and Behavioural Sciences
摘要：
在他的开创性著作《囚犯们在经营庇护所：为什么高科技产品让我们疯狂，以及如何恢复理智》（2004年，美国印第安纳州萨姆斯印第安纳波利斯）中，艾伦·库珀认为，软件通常设计得很差的一个主要原因（从用户的角度来看）是程序员负责设计决策，而不是交互设计师。因此，程序员为自己而不是为目标受众设计软件，他将这种现象称为“囚犯管理庇护所”。本文认为，可解释的人工智能也面临着类似的命运。虽然可解释人工智能的重新出现是积极的，但本文认为，我们大多数人作为人工智能研究人员正在为自己而不是为预期用户构建解释性代理。但是，如果研究人员和实践者能够理解、采用、实施和改进哲学、心理学和认知科学领域大量有价值的研究成果中的模型，并且如果对这些模型的评估更多地关注人而不是技术，那么可解释的人工智能就更有可能成功。通过对文献的简单扫描，我们证明了将更多来自社会和行为科学的结果注入可解释的人工智能中有相当大的空间，并提出了与可解释的人工智能相关的这些领域的一些关键结果。
英文摘要：
In his seminal book `The Inmates are Running the Asylum: Why High-Tech Products Drive Us Crazy And How To Restore The Sanity' [2004, Sams Indianapolis, IN, USA], Alan Cooper argues that a major reason why software is often poorly designed (from a user perspective) is that programmers are in charge of design decisions, rather than interaction designers. As a result, programmers design software for themselves, rather than for their target audience, a phenomenon he refers to as the `inmates running the asylum'. This paper argues that explainable AI risks a similar fate. While the re-emergence of explainable AI is positive, this paper argues most of us as AI researchers are building explanatory agents for ourselves, rather than for the intended users. But explainable AI is more likely to succeed if researchers and practitioners understand, adopt, implement, and improve models from the vast and valuable bodies of research in philosophy, psychology, and cognitive science, and if evaluation of these models is focused more on people than on technology. From a light scan of literature, we demonstrate that there is considerable scope to infuse more results from the social and behavioural sciences into explainable AI, and present some key results from these fields that are relevant to explainable AI.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
