This paper addresses how to combine human and AI predictions in classification tasks where both contribute to a final label. Prior work assumed conditional independence and used Bayes rule with calibrated probabilities from both parties. The current work extends or refines this combination method for practical deployment in Human-AI teams on classification tasks. (Abstract appears truncated, so full contribution is unclear.)
Main takeaways:
- Focuses on combining human (deterministic) and model (probabilistic) outputs for classification
- Assumes human and model predictions are conditionally independent given ground truth
- Uses instance-level model probabilities and class-level human calibration
- Aims for cost-effective performance in Human-AI collaboration
- (Abstract cut off—unclear what the novel contribution is beyond prior Bayes-rule methods)