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Natural Language Generation enhances human decision-making with uncertain information

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arxiv 1606.03254 v2 pith:K7LVRL7V submitted 2016-06-10 cs.CL cs.AI

classification cs.CLcs.AI
keywords decision-makingpresentationsbetterdatauncertainaveragecomparedgeneration
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Decision-making is often dependent on uncertain data, e.g. data associated with confidence scores or probabilities. We present a comparison of different information presentations for uncertain data and, for the first time, measure their effects on human decision-making. We show that the use of Natural Language Generation (NLG) improves decision-making under uncertainty, compared to state-of-the-art graphical-based representation methods. In a task-based study with 442 adults, we found that presentations using NLG lead to 24% better decision-making on average than the graphical presentations, and to 44% better decision-making when NLG is combined with graphics. We also show that women achieve significantly better results when presented with NLG output (an 87% increase on average compared to graphical presentations).

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  1. On the Limits of Selective AI Prediction: A Case Study in Clinical Decision Making

    cs.HC 2025-08 conditional novelty 6.0 of 10

    Selective prediction keeps clinicians' overall accuracy roughly intact but shifts errors toward underdiagnosis (18% more missed) and undertreatment (35% more missed) when the AI abstains.

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