Why and why not explanations improve the intelligibility of context-aware intelligent systems
Why and why not explanations improve the intelligibility of context-aware intelligent systems
复制标题
为什么和为什么不解释可以提高上下文感知智能系统的可理解性
DOI：
10.1145/1518701.1519023
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发表时间：
2009
期刊：
影响因子：
--
通讯作者：
Daniel Avrahami
中科院分区：
文献类型：
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作者：
Brian Y. Lim;A. Dey;Daniel Avrahami
上下文感知智能系统采用隐式输入，并根据用户很少清楚的复杂规则和机器学习模型做出决策。系统清晰度的缺乏会导致用户失去对这些系统的信任、满意度和接受度。然而，自动提供有关系统决策过程的解释可以帮助缓解这个问题。在本文中，我们展示了一项有 200 多名参与者参与的对照研究的结果，其中检验了不同类型解释的有效性。向参与者展示了系统操作的示例以及各种自动生成的解释，然后测试了他们对系统的理解。例如，我们表明，描述系统为何以某种方式运行的解释可以带来更好的理解和更强烈的信任感。描述系统为何没有以某种方式运行的解释会导致理解程度较低，但性能却足够。我们讨论了在现实世界的上下文感知应用程序中使用我们的发现的含义。
Context-aware intelligent systems employ implicit inputs, and make decisions based on complex rules and machine learning models that are rarely clear to users. Such lack of system intelligibility can lead to loss of user trust, satisfaction and acceptance of these systems. However, automatically providing explanations about a system's decision process can help mitigate this problem. In this paper we present results from a controlled study with over 200 participants in which the effectiveness of different types of explanations was examined. Participants were shown examples of a system's operation along with various automatically generated explanations, and then tested on their understanding of the system. We show, for example, that explanations describing why the system behaved a certain way resulted in better understanding and stronger feelings of trust. Explanations describing why the system did not behave a certain way, resulted in lower understanding yet adequate performance. We discuss implications for the use of our findings in real-world context-aware applications.