Do People Engage Cognitively with AI? Impact of AI Assistance on Incidental Learning
Do People Engage Cognitively with AI? Impact of AI Assistance on Incidental Learning
复制标题
人们会以认知方式参与人工智能吗？
DOI：
10.1145/3490099.3511138
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Mamykina, Lena
中科院分区：
文献类型：
--
作者：
Gajos, Krzysztof Z.;Mamykina, Lena
当人们在做出困难决定时接受建议时，他们往往会在那一刻做出更好的决定，并在这个过程中增加他们的知识。然而，这种附带学习只有在人们认知地参与他们收到的信息并深思熟虑地处理这些信息时才能发生。人们如何处理他们从人工智能那里得到的信息和建议，他们是否深入地参与其中以实现学习？为了回答这些问题，我们进行了三个实验，在这些实验中，人们被要求做出营养决策，并接受模拟的人工智能建议和解释。在第一个实验中，我们发现，当人们在做出选择之前同时得到建议和解释时，他们做出的决定比没有得到这样的帮助时更好，但他们没有学习。在第二个实验中，参与者首先做出了自己的选择，然后才看到人工智能的建议和解释;这种情况也导致了决策的改善，但没有学习。然而，在我们的第三个实验中，参与者只得到了人工智能的解释，但没有建议，他们必须自己做出决定。这种情况导致了更准确的决策和学习收益。我们假设，在这种情况下，学习的收获是由于更深入地参与了做出决定所需的解释。这项工作提供了一些迄今为止最直接的证据，表明将解释与人工智能生成的建议一起提供可能不足以确保人们仔细地使用人工智能提供的信息。这项工作还提出了一种技术，可以实现附带学习，并可以帮助人们更仔细地处理人工智能的建议和解释。
When people receive advice while making difficult decisions, they often make better decisions in the moment and also increase their knowledge in the process. However, such incidental learning can only occur when people cognitively engage with the information they receive and process this information thoughtfully. How do people process the information and advice they receive from AI, and do they engage with it deeply enough to enable learning? To answer these questions, we conducted three experiments in which individuals were asked to make nutritional decisions and received simulated AI recommendations and explanations. In the first experiment, we found that when people were presented with both a recommendation and an explanation before making their choice, they made better decisions than they did when they received no such help, but they did not learn. In the second experiment, participants first made their own choice, and only then saw a recommendation and an explanation from AI; this condition also resulted in improved decisions, but no learning. However, in our third experiment, participants were presented with just an AI explanation but no recommendation and had to arrive at their own decision. This condition led to both more accurate decisions and learning gains. We hypothesize that learning gains in this condition were due to deeper engagement with explanations needed to arrive at the decisions. This work provides some of the most direct evidence to date that it may not be sufficient to include explanations together with AI-generated recommendation to ensure that people engage carefully with the AI-provided information. This work also presents one technique that enables incidental learning and, by implication, can help people process AI recommendations and explanations more carefully.
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DOI：
--
发表时间：
2019-07
期刊：
--
影响因子：
--
作者：
Benjamin Krarup;Michael Cashmore;D. Magazzeni;Tim Miller
通讯作者：
Benjamin Krarup;Michael Cashmore;D. Magazzeni;Tim Miller
第二版
DOI：
--
发表时间：
2008
期刊：
影响因子：
--
作者：
Fujita J. Springer;Berlin;Schwab;M.(ed.)
通讯作者：
M.(ed.)
影响因子：
3.7
作者：
Huber, Bernd;Gajos, Krzysztof Z.
通讯作者：
Gajos, Krzysztof Z.
混合机器学习和人类判断的效果
影响因子：
22.7
作者：
Michelle Vaccaro;Jim Waldo
通讯作者：
Jim Waldo
实用主义的解释理论
影响因子：
3.1
作者：
R. Tuomela
通讯作者：
R. Tuomela