Using AI Uncertainty Quantification to Improve Human Decision-Making
This addresses the problem of enhancing decision-making for users of AI systems, though it is incremental as it builds on existing research on AI-human interaction.
The study tackled the problem of improving human decision-making by incorporating AI uncertainty quantification (UQ) alongside predictions, finding that UQ enhanced performance in online experiments compared to using predictions alone.
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.