AI的类别不平衡会误导人类判断，加剧认知偏误。
Biased Minds Meet Biased AI: How Class Imbalance Shapes Appropriate Reliance and Interacts with Human Base Rate Neglect
- 用不平衡数据训练的AI影响人类对它的信任程度
- 人类忽视基础概率时，错误更易被放大
- 适合研究人机协作中偏见交互的学者
人类在决策中越来越多地依赖人工智能（AI），但AI和人类都存在偏见。尽管各自偏见已有广泛研究，本文探讨了它们之间的复杂互动。我们通过一项在线双因素实验（N=46）考察了类别不平衡这一AI偏见如何影响人们对基于AI决策支持系统适当依赖的能力，并分析其与人类基础概率忽视偏见的相互作用。参与者使用一个在平衡或不平衡数据集上训练的AI系统诊断三种疾病。结果发现，类别不平衡破坏了用户对AI依赖程度的准确校准。此外，我们观察到类别不平衡与基础概率忽视之间存在相互强化效应，提供了人机偏见复合作用的实证证据。基于此，本文倡导采用交互视角，并呼吁进一步研究人机交互中偏见的协同放大机制。
原文摘要 · Abstract (English)
Humans increasingly interact with artificial intelligence (AI) in decision-making. However, both AI and humans are prone to biases. While AI and human biases have been studied extensively in isolation, this paper examines their complex interaction. Specifically, we examined how class imbalance as an AI bias affects people's ability to appropriately rely on an AI-based decision-support system, and how it interacts with base rate neglect as a human bias. In a within-subject online study (N= 46), participants classified three diseases using an AI-based decision-support system trained on either a balanced or unbalanced dataset. We found that class imbalance disrupted participants' calibration of AI reliance. Moreover, we observed mutually reinforcing effects between class imbalance and base rate neglect, offering evidence of a compound human-AI bias. Based on these findings, we advocate for an interactionist perspective and further research into the mutually reinforcing effects of biases in human-AI interaction.
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