Using AI Uncertainty Quantification to Improve Human Decision-Making

• URL: http://arxiv.org/abs/2309.10852v2
• Date: Tue, 6 Feb 2024 16:59:17 GMT
• Title: Using AI Uncertainty Quantification to Improve Human Decision-Making
• Authors: Laura R. Marusich, Jonathan Z. Bakdash, Yan Zhou, Murat Kantarcioglu
• Abstract summary: AI Uncertainty Quantification (UQ) has the potential to improve human decision-making beyond AI predictions alone. We evaluated the impact on human decision-making for instance-level UQ, using a strict scoring rule, in two online behavioral experiments.
• Score: 14.878886078377562
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: AI Uncertainty Quantification (UQ) has the potential to improve human decision-making beyond AI predictions alone by providing additional probabilistic information to users. The majority of past research on AI and human decision-making has concentrated on model explainability and interpretability, with little focus on understanding the potential impact of UQ on human decision-making. We evaluated the impact on human decision-making for instance-level UQ, calibrated using a strict scoring rule, in two online behavioral experiments. In the first experiment, our results showed that UQ was beneficial for decision-making performance compared to only AI predictions. In the second experiment, we found UQ had generalizable benefits for decision-making across a variety of representations for probabilistic information. These results indicate that implementing high quality, instance-level UQ for AI may improve decision-making with real systems compared to AI predictions alone.

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