Deciding Fast and Slow: The Role of Cognitive Biases in AI-assisted Decision-making

CHARVI RASTOGI∗ , YUNFENG ZHANG∗ , DENNIS WEI, KUSH R.  AMIT DHURANDHAR,  RICHARD TOMSETT

Abstract: Several strands of research have aimed to bridge the gap between artificial intelligence (AI) and human decision-makers in AI-assisted decision-making, where humans are the consumers of AI model predictions and the ultimate decision-makers in high-stakes applications. However, people’s perception and understanding are often distorted by their cognitive biases, such as confirmation bias, anchoring bias, availability bias, to name a few. In this work, we use knowledge from the field of cognitive science to account for  cognitive biases in the human-AI collaborative decision-making setting, and mitigate their negative effects on collaborative performance. To this end, we mathematically model cognitive biases and provide a general framework through which researchers and practitioners can understand the interplay between cognitive biases and human-AI accuracy. We then focus specifically on anchoring bias, a bias commonly encountered in human-AI collaboration. We implement a time-based de-anchoring strategy and conduct our first user experiment that validates its effectiveness in human-AI collaborative decision-making. With this result, we design a time allocation strategy for a resource-constrained setting that achieves optimal human-AI collaboration under some assumptions. We, then, conduct a second user experiment which shows that our time allocation strategy with explanation can effectively de-anchor the human and improve collaborative performance when the AI model has low confidence and is incorrect.

Keywords: Human-AI collaboration; decision support; cognitive bias; anchoring bias

快慢决策： 认知偏差在人工智能辅助决策中的作用

CHARVI RASTOGI∗ , YUNFENG ZHANG∗ , DENNIS WEI, KUSH R.  AMIT DHURANDHAR,  RICHARD TOMSETT

摘要：在人工智能辅助决策中，人类是人工智能模型预测的消费者，也是高风险应用中的最终决策者。然而，人们的感知和理解往往会被其认知偏差所扭曲，例如确认偏差、锚定偏差、可用性偏差等等。在这项工作中，我们利用认知科学领域的知识来解释人类与人工智能协作决策环境中的认知偏差，并减轻它们对协作绩效的负面影响。为此，我们对认知偏差进行了数学建模，并提供了一个总体框架，研究人员和从业人员可通过该框架了解认知偏差与人类-人工智能准确性之间的相互作用。然后，我们将重点放在锚定偏差上，这是人类与人工智能协作中经常遇到的一种偏差。我们实施了一种基于时间的去锚定策略，并进行了首次用户实验，验证了该策略在人类-人工智能协作决策中的有效性。根据这一结果，我们为资源受限的环境设计了一种时间分配策略，在一些假设条件下实现了最佳的人机协作。然后，我们进行了第二次用户实验，结果表明，当人工智能模型置信度低且不正确时，我们的时间分配策略加上解释可以有效地去锚定人类，并提高协作性能。

关键词：人机协作；决策支持；认知偏差；锚定偏差

来源：Deciding Fast and Slow: The Role of Cognitive Biases in AI-assisted Decision-making. Proc. ACM Hum.-Comput. Interact. 6, CSCW1, Article 83 (April 2022), 22 pages. https://doi.org/10.1145/3512930