mlsanityData to 14 Oct 2026

Using AI Uncertainty Quantification to Improve Human Decision-Making

arXiv 2309.10852 ↗First posted 19 Sep 2023cs.AI cs.HCcited by 11

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Abstract (arXiv)

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.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 25 of its 59 references that are in the map. First posted on arXiv 19 Sep 2023.

Builds on

25 of 59 references.

References · 25

  1. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,926
  2. 2017What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?cited by 2,352
  3. 2020A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challengescited by 819
  4. 2018Evidential Deep Learning to Quantify Classification Uncertaintycited by 732
  5. 2020Bayesian Deep Learning and a Probabilistic Perspective of Generalizationcited by 480
  6. 2014Stochastic Gradient Hamiltonian Monte Carlocited by 343
  7. 2020Effect of Confidence and Explanation on Accuracy and Trust Calibration in AI-Assisted Decision Makingcited by 281
  8. 2018Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networkscited by 280
  9. 2018To Trust Or Not To Trust A Classifiercited by 207
  10. 2017Multiplicative Normalizing Flows for Variational Bayesian Neural Networkscited by 157
  11. 2019From Variational to Deterministic Autoencoderscited by 133
  12. 2020Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertaintycited by 129
13 more
  1. 2021DEUP: Direct Epistemic Uncertainty Predictioncited by 104
  2. 2021Towards a Science of Human-AI Decision Making: A Survey of Empirical Studiescited by 86
  3. 2018What can AI do for me: Evaluating Machine Learning Interpretations in Cooperative Playcited by 53
  4. 2020Does Explainable Artificial Intelligence Improve Human Decision-Making?cited by 49
  5. 2019Accurate Uncertainty Estimation and Decomposition in Ensemble Learningcited by 42
  6. 2016On the Convergence of Stochastic Gradient MCMC Algorithms with High-Order Integratorscited by 35
  7. 2019Maximizing Overall Diversity for Improved Uncertainty Estimates in Deep Ensemblescited by 30
  8. 2022A Meta-Analysis of the Utility of Explainable Artificial Intelligence in Human-AI Decision-Makingcited by 29
  9. 2019A Human-Grounded Evaluation of SHAP for Alert Processingcited by 23
  10. 2022UncertaINR: Uncertainty Quantification of End-to-End Implicit Neural Representations for Computed Tomographycited by 19
  11. 2022Ensembles for Uncertainty Estimation: Benefits of Prior Functions and Bootstrappingcited by 19
  12. 2022UQGAN: A Unified Model for Uncertainty Quantification of Deep Classifiers trained via Conditional GANscited by 16
  13. 2020Liberty or Depth: Deep Bayesian Neural Nets Do Not Need Complex Weight Posterior Approximationscited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 0; 2025: 6; 2026: 4.

11 papers.

  1. 2025From Calibration to Collaboration: LLM Uncertainty Quantification Should Be More Human-Centeredcited by 21
  2. 2023Online Algorithms with Uncertainty-Quantified Predictionscited by 9
  3. 2025Human-AI Collaborative Uncertainty Quantificationcited by 7
  4. 2025HybridFlow: Quantification of Aleatoric and Epistemic Uncertainty with a Single Hybrid Modelcited by 3
  5. 2025Exploring the Impact of Explainable AI and Cognitive Capabilities on Users' Decisionscited by 1
  6. 2025DeCoDe: Defer-and-Complement Decision-Making via Decoupled Concept Bottleneck Modelscited by 0
  7. 2025Parameter Space Analysis through Guided Visual Interpolationscited by 0
  8. 2026Fine-grained Approaches for Confidence Calibration of LLMs in Automated Code Revisioncited by 0
  9. 2026Human Decision-Making with Persuasive and Narrative LLM Explanationscited by 0
  10. 2026Localizing Input Uncertainty Quantification for Large Language Models via Shapley Valuescited by 0
  11. 2026Explainable Uncertainty Estimation for Reliable Medical AIcited by 0