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Selective AI prediction reduces automation bias but increases missed diagnoses

By PulseAugur Editorial · [1 sources] ·

A new study published on arXiv explores the effectiveness of selective prediction in AI systems to mitigate automation bias. The research found that while selective prediction can reduce the negative impact of inaccurate AI predictions on human decision-making, it also leads to an increase in false negatives. In a clinical context, clinicians using selective prediction systems were more likely to miss diagnoses and treatments compared to those without AI assistance. AI

IMPACT This research highlights potential trade-offs in AI system design, suggesting that while reducing over-reliance on AI is beneficial, it may introduce new error patterns that require careful consideration in deployment.

RANK_REASON Academic paper detailing a new approach to AI interaction and its effects. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Selective AI prediction reduces automation bias but increases missed diagnoses

COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Sarah Jabbour, David Fouhey, Nikola Banovic, Stephanie D. Shepard, Ella Kazerooni, Michael W. Sjoding, Jenna Wiens ·

    Selective Prediction Reduces the Negative Effects of Automation Bias Overall but Increases False Negatives

    arXiv:2508.07617v2 Announce Type: replace-cross Abstract: AI has the potential to augment human decision making. However, even high-performing models can produce inaccurate predictions when deployed. These inaccuracies, combined with automation bias, where humans overrely on AI p…

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