mlsanityData to 7 Oct 2026

Human-AI Collaboration in Decision-Making: Beyond Learning to Defer

arXiv 2206.13202 ↗First posted 27 Jun 2022cs.LG cs.AI cs.HCcited by 16

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Abstract (arXiv)

Human-AI collaboration (HAIC) in decision-making aims to create synergistic teaming between human decision-makers and AI systems. Learning to defer (L2D) has been presented as a promising framework to determine who among humans and AI should make which decisions in order to optimize the performance and fairness of the combined system. Nevertheless, L2D entails several often unfeasible requirements, such as the availability of predictions from humans for every instance or ground-truth labels that are independent from said humans. Furthermore, neither L2D nor alternative approaches tackle fundamental issues of deploying HAIC systems in real-world settings, such as capacity management or dealing with dynamic environments. In this paper, we aim to identify and review these and other limitations, pointing to where opportunities for future research in HAIC may lie.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Builds on 16 of its 30 references that are in the map. First posted on arXiv 27 Jun 2022.

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16 of 30 references.

References · 16

  1. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,416
  2. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,888
  3. 2016A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networkscited by 2,362
  4. 2020Performative Predictioncited by 318
  5. 2020Consistent Estimators for Learning to Defer to an Expertcited by 262
  6. 2017Predict Responsibly: Improving Fairness and Accuracy by Learning to Defercited by 228
  7. 2019Addressing Failure Prediction by Learning Model Confidencecited by 183
  8. 2020Learning to Complement Humanscited by 123
  9. 2021Towards Unbiased and Accurate Deferral to Multiple Expertscited by 71
  10. 2022Calibrated Learning to Defer with One-vs-All Classifierscited by 68
  11. 2021How can I choose an explainer? An Application-grounded Evaluation of Post-hoc Explanationscited by 58
  12. 2021Human-AI Collaboration with Bandit Feedbackcited by 36
4 more
  1. 2020Selective Classification Can Magnify Disparities Across Groupscited by 35
  2. 2018Learning under selective labels in the presence of expert consistencycited by 18
  3. 2022Designing Closed Human-in-the-loop Deferral Pipelinescited by 11
  4. 2019A Comprehensive Survey on Machine Learning Techniques and User Authentication Approaches for Credit Card Fraud Detectioncited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 4; 2024: 8; 2025: 1; 2026: 3.

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  1. 2023Human-AI Collaboration: The Effect of AI Delegation on Human Task Performance and Task Satisfactioncited by 32
  2. 2024Learning to Defer to a Population: A Meta-Learning Approachcited by 26
  3. 2023Learning to Defer with Limited Expert Predictionscited by 25
  4. 2024Evaluating Human-AI Collaboration: A Review and Methodological Frameworkcited by 25
  5. 2024Human Expertise in Algorithmic Predictioncited by 13
  6. 2024Cost-Sensitive Learning to Defer to Multiple Experts with Workload Constraintscited by 12
  7. 2024AI, Meet Human: Learning Paradigms for Hybrid Decision Making Systemscited by 7
  8. 2024Neuro-Symbolic AI for Military Applicationscited by 7
  9. 2023Learning to Complement with Multiple Humanscited by 5
  10. 2024Human Delegation Behavior in Human-AI Collaboration: The Effect of Contextual Informationcited by 5
  11. 2023FiFAR: A Fraud Detection Dataset for Learning to Defercited by 3
  12. 2024Integrating Expert Judgment and Algorithmic Decision Making: An Indistinguishability Frameworkcited by 3
4 more
  1. 2025Decision-Making with Deliberation: Meta-reviewing as a Document-grounded Dialoguecited by 3
  2. 2026People-Centred Medical Image Analysis via Fairness-Aware Human-AI Cooperationcited by 0
  3. 2026PLACO: A Multi-Stage Framework for Cost-Effective Performance in Human-AI Teamscited by 0
  4. 2026Calibrated Trust, Not Sharper Prediction: An Empirical Test of Uncertainty Fusioncited by 0