Too Much of the Same: From Algorithmic to Human Bias in Learning to Defer

2026-08-28

AI Summary

研究探讨了学习转交(LtD)策略在不平衡数据集上的类依赖采样偏差问题，通过用户实验揭示该偏差可能激发人类认知偏见，影响决策准确性。

Abstract

Learning to Defer (LtD) extends supervised learning by allowing a Machine Learning (ML) model to defer harder or less confident decisions to a human expert. Despite being geared for human-AI collaboration, LtD strategies neglect the potential negative interference of human cognitive biases. Our contribution is twofold. First, we demonstrate that standard LtD strategies show class-dependent sampling bias in classification tasks in practice, and thus may disproportionately defer the minority classes when applied to imbalanced datasets. Second, we show that such asymmetries in task delegation may trigger human biases, ultimately leading to poorer downstream decision making. Specifically, we conduct a user study ($N=226$) where participants complete a classification task on a set of deferred items, with conditions presenting different levels of class imbalance. Our results show that participants exposed to a highly imbalanced rejection set achieved lower classification accuracy in the majority class compared to those exposed to a more balanced set, regardless of which class constituted the majority. Exploratory analyses suggest that this may be an instance of the Test-taker's effect, which stems from a mismatch between the actual distribution of classes and the participants' expectations about that distribution. Finally, we discuss the implications of these findings for the deployment of LtD algorithms.

Problem

• Learning to Defer
• class imbalance
• human bias
• classification task
• sampling bias

Innovation

• Learning to Defer
• Class Imbalance
• Human Bias
• Test-taker's Effect