AI-Assisted Decision-making: a Cognitive Modeling Approach to Infer Latent Reliance Strategies
AI-Assisted Decision-making: a Cognitive Modeling Approach to Infer Latent Reliance Strategies
复制标题
人工智能辅助决策：推断潜在依赖策略的认知建模方法
DOI：
10.1007/s42113-022-00157-y
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Steyvers, Mark
中科院分区：
文献类型：
--
作者：
Tejeda, Heliodoro;Kumar, Aakriti;Smyth, Padhraic;Steyvers, Mark
人工智能在各种决策应用中随时可供人类使用。为了充分理解这种联合决策的功效，首先要了解人类对人工智能的依赖。然而，如何研究共同决策与如何在现实世界中实践之间存在脱节。通常情况下，研究人员会要求人类在获得人工智能的帮助之前做出独立的决定。这样做是为了明确人工智能辅助对人类决策的影响。我们开发了一个认知模型，使我们能够在不要求人类做出独立决定的情况下推断人类对人工智能援助的潜在依赖策略。我们通过两个行为实验验证了模型的预测。第一个实验遵循当前模式，即人类在决策问题上获得人工智能帮助。第二个实验遵循了一个平等的范例，在人工智能提供帮助之前，人类对决策问题提供了独立的判断。该模型预测的依赖策略与人类在两种实验范式中使用的策略密切相关。我们的模型提供了一种原则性的方法来推断对人工智能援助的依赖，并可用于扩大人类与人工智能合作的调查范围。
AI assistance is readily available to humans in a variety of decision-making applications. In order to fully understand the efficacy of such joint decision-making, it is important to first understand the human’s reliance on AI. However, there is a disconnect between how joint decision-making is studied and how it is practiced in the real world. More often than not, researchers ask humans to provide independent decisions before they are shown AI assistance. This is done to make explicit the influence of AI assistance on the human’s decision. We develop a cognitive model that allows us to infer thelatentreliance strategy of humans on AI assistance without asking the human to make an independent decision. We validate the model’s predictions through two behavioral experiments. The first experiment follows aconcurrentparadigm where humans are shown AI assistance alongside the decision problem. The second experiment follows asequentialparadigm where humans provide an independent judgment on a decision problem before AI assistance is made available. The model’s predicted reliance strategies closely track the strategies employed by humans in the two experimental paradigms. Our model provides a principled way to infer reliance on AI-assistance and may be used to expand the scope of investigation on human-AI collaboration.
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影响因子：
15.2
作者：
Patel, Bhavik N.;Rosenberg, Louis;Lungren, Matthew
通讯作者：
Lungren, Matthew
DOI：
10.1145/3411764.3445562
发表时间：
2021-05
期刊：
Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
影响因子：
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作者：
Zhuoran Lu;Ming Yin
通讯作者：
Zhuoran Lu;Ming Yin
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作者：
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影响因子：
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作者：
J. Park;Richard B. Berlin;A. Kirlik;Karrie Karahalios
通讯作者：
Karrie Karahalios
DOI：
--
发表时间：
2021
期刊：
Proceedings of the Annual Meeting of the Cognitive Science Society
影响因子：
--
作者：
Kumar, A.;Patel, T.;Benjamin, A.;Steyvers, M.
通讯作者：
Steyvers, M.