Explainable AI is Dead, Long Live Explainable AI!: Hypothesis-driven Decision Support using Evaluative AI
Explainable AI is Dead, Long Live Explainable AI!: Hypothesis-driven Decision Support using Evaluative AI
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可解释的人工智能已死，可解释的人工智能万岁！：使用评估性人工智能的假设驱动决策支持
DOI：
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Tim Miller
中科院分区：
文献类型：
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作者：
Tim Miller
在本文中，我们主张从当前的可解释人工智能（XAI）模型进行范式转变，这可能会对更好的人类决策产生反作用。在早期的决策支持系统中，我们假设我们可以向人们提供建议，他们会考虑这些建议，然后在需要时遵循这些建议。然而，研究发现，人们经常忽视建议，因为他们不信任他们;或者更糟糕的是，人们盲目地遵循他们，即使建议是错误的。可解释的人工智能通过帮助人们理解模型如何以及为什么给出某些建议来缓解这一问题。然而，最近的研究表明，人们并不总是充分利用可解释性工具来帮助改善决策。人们会接受建议和解释的假设已被证明是没有根据的。我们认为这是因为我们没有考虑到两件事。首先，建议（及其解释）从人类决策者那里获得控制权，限制了他们的代理权。其次，给出建议和解释与人们做决策时所采用的认知过程不一致。这份立场文件提出了一个新的概念框架，称为评估AI，用于可解释的决策支持。这是一个机器在环的范例，其中决策支持工具提供支持和反对人们所做决策的证据，而不是提供接受或拒绝的建议。我们认为，这缓解了过度依赖和依赖决策支持工具的问题，并更好地利用人类的专业知识进行决策。
In this paper, we argue for a paradigm shift from the current model of explainable artificial intelligence (XAI), which may be counter-productive to better human decision making. In early decision support systems, we assumed that we could give people recommendations and that they would consider them, and then follow them when required. However, research found that people often ignore recommendations because they do not trust them; or perhaps even worse, people follow them blindly, even when the recommendations are wrong. Explainable artificial intelligence mitigates this by helping people to understand how and why models give certain recommendations. However, recent research shows that people do not always engage with explainability tools enough to help improve decision making. The assumption that people will engage with recommendations and explanations has proven to be unfounded. We argue this is because we have failed to account for two things. First, recommendations (and their explanations) take control from human decision makers, limiting their agency. Second, giving recommendations and explanations does not align with the cognitive processes employed by people making decisions. This position paper proposes a new conceptual framework called Evaluative AI for explainable decision support. This is a machine-in-the-loop paradigm in which decision support tools provide evidence for and against decisions made by people, rather than provide recommendations to accept or reject. We argue that this mitigates issues of over- and under-reliance on decision support tools, and better leverages human expertise in decision making.
人们会以认知方式参与人工智能吗？
DOI：
10.1145/3490099.3511138
发表时间：
2022
期刊：
27th International Conference on Intelligent User Interfaces (IUI ’22
影响因子：
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作者：
Gajos, Krzysztof Z.;Mamykina, Lena
通讯作者：
Mamykina, Lena