Stakeholders in Explainable AI
Stakeholders in Explainable AI
复制标题
可解释人工智能的利益相关者
DOI：
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复制
发表时间：
2018
期刊：
影响因子：
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通讯作者：
Supriyo Chakraborty
中科院分区：
文献类型：
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作者：
A. Preece;Daniel Harborne;Dave Braines;Richard J. Tomsett;Supriyo Chakraborty
人们普遍的共识是对人造很重要
智能（AI）和机器学习系统要解释
和/或可以解释的
关于“可解释”和“可解释”的含义的共识。
在本文中，我们认为这种缺乏共识
由于存在几个不同的利益相关者社区。
我们注意到，虽然个人的关注
社区是广泛兼容的，它们并不完全相同，
这引起了解释的不同意图和要求/
解释性。
验证和验证之间的区别以及认识论
嘲笑已知/未知数之间的区别
除了利益相关者社区的担忧和突出显示
他们的焦点重叠或分歧的区域没有
本文作者的目的是“支持方” - 我们
在多个程度上算作自己的成员
社区 - 而是为了帮助消除利益相关者的歧义
意思是当他们问“为什么？” AI时。
There is general consensus that it is important for artificial
intelligence (AI) and machine learning systems to be explainable
and/or interpretable. However, there is no general
consensus over what is meant by ‘explainable’ and ‘interpretable’.
In this paper, we argue that this lack of consensus
is due to there being several distinct stakeholder communities.
We note that, while the concerns of the individual
communities are broadly compatible, they are not identical,
which gives rise to different intents and requirements for explainability/
interpretability. We use the software engineering
distinction between validation and verification, and the epistemological
distinctions between knowns/unknowns, to tease
apart the concerns of the stakeholder communities and highlight
the areas where their foci overlap or diverge. It is not
the purpose of the authors of this paper to ‘take sides’ — we
count ourselves as members, to varying degrees, of multiple
communities — but rather to help disambiguate what stakeholders
mean when they ask ‘Why?’ of an AI.
DOI：
10.1109/test.2018.8624792
发表时间：
2018-02
期刊：
2018 IEEE International Test Conference (ITC)
影响因子：
--
作者：
Klas Leino;Linyi Li;S. Sen;Anupam Datta;Matt Fredrikson
通讯作者：
Klas Leino;Linyi Li;S. Sen;Anupam Datta;Matt Fredrikson
影响因子：
22.7
作者：
Goodfellow, Ian;McDaniel, Patrick;Papernot, Nicolas
通讯作者：
Papernot, Nicolas