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Proceedings of the National Academy of Sciences
Bayesian modeling of human–AI complementarity
Research summary
A Bayesian framework models when human and machine classifiers can complement one another while accounting for differences in expressed confidence. The result is useful for hybrid design but is centered on classification tasks.
Evidence strengths
- Explicit human-machine comparison
- Bayesian modeling of confidence
- Reproducible code and data
Limitations
- Centered on classification tasks
- Modeling assumptions may not transfer to open-ended collaboration
Topics
- Human-AI Interaction
- Artificial Intelligence
- Prediction
- Cognitive Science
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