Learning to Defer: A Survey
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| Format: | Recurso digital |
| Langue: | anglais |
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Zenodo
2025
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| _version_ | 1866902233291948032 |
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| author | Strong, Joshua Sun, Emma Rogers, Harry Higham, Helen Noble, Alison |
| author_facet | Strong, Joshua Sun, Emma Rogers, Harry Higham, Helen Noble, Alison |
| contents | <p>Learning to defer (L2D) enables AI systems to choose between autonomous prediction and deferral to experts. This survey consolidates the fast-growing literature through a four-branch taxonomy: methodological frameworks; optimization and theory; task generalizations; and real-world adaptations. We outline contrasts between score-based and predictor–rejector formulations; one-stage, two-stage, and post-hoc training; unify surrogate losses with theoretical guarantees; and synthesize extensions to regression, multi-task prediction, top-$k$ committees, sequential settings, and causal pipelines. Practical considerations include limited annotations, dynamic expert pools, workload/budget control, fairness, interpretability, robustness, and uncertainty handling, concluding with open challenges for reliable human–AI decision systems.<br><br><strong>Note:</strong> This is the preprint version of a manuscript currently under review. It has not undergone peer review.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17843044 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Learning to Defer: A Survey Strong, Joshua Sun, Emma Rogers, Harry Higham, Helen Noble, Alison Machine Learning Artificial Intelligence Human-AI Collaboration Learning to Defer Survey <p>Learning to defer (L2D) enables AI systems to choose between autonomous prediction and deferral to experts. This survey consolidates the fast-growing literature through a four-branch taxonomy: methodological frameworks; optimization and theory; task generalizations; and real-world adaptations. We outline contrasts between score-based and predictor–rejector formulations; one-stage, two-stage, and post-hoc training; unify surrogate losses with theoretical guarantees; and synthesize extensions to regression, multi-task prediction, top-$k$ committees, sequential settings, and causal pipelines. Practical considerations include limited annotations, dynamic expert pools, workload/budget control, fairness, interpretability, robustness, and uncertainty handling, concluding with open challenges for reliable human–AI decision systems.<br><br><strong>Note:</strong> This is the preprint version of a manuscript currently under review. It has not undergone peer review.</p> |
| title | Learning to Defer: A Survey |
| topic | Machine Learning Artificial Intelligence Human-AI Collaboration Learning to Defer Survey |
| url | https://doi.org/10.5281/zenodo.17843044 |