Too Much of the Same: From Algorithmic to Human Bias in Learning to Defer
2.80T1 sourcearXiv cs.HC
arXiv cs.HCoriginal source ↗
Source record
Published by arXiv cs.HC (T1 source). The original is at https://arxiv.org/abs/2608.28050.
Pipeline notes
The summary and note below are generated by the signal pipeline — they are Beyond Desk’s reading, not quotations from the source.
SummaryThe paper shows that Learning to Defer (LtD) strategies create class-dependent sampling bias, disproportionately routing minority-class items to human experts. A user study (N=226) finds that imbalanced deferred item sets trigger human cognitive bias, reducing accuracy in majority-class classification.
Why it mattersConcrete evidence that how an AI chooses to defer decisions can distort human judgment downstream, relevant for designing human-AI collaboration workflows.
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