The authors tackle a problem that comes up when you want to use AI model predictions as prior beliefs for Bayesian inference: the AI might be wrong, and that error gets baked into your statistical conclusions. They propose "rectifying" the AI-generated distribution before using it as a prior—essentially correcting for known biases in the model's outputs. They show that this rectified prior reduces bias in the resulting posterior estimates, improves the coverage of credible intervals (the intervals actually contain the true value as often as they should), and boosts predictive performance on a real medical classification task.
Main takeaways:
- Standard practice of using AI predictions directly as priors can propagate model errors into your statistical inference
- The rectification step corrects the AI's output distribution before building a prior, reducing downstream bias
- They prove Gaussian asymptotics for the posterior and derive expressions for centering bias under their framework
- Empirical results show better credible interval coverage and improved predictive performance on skin disease classification
- The method works with flexible prior structures like Dirichlet processes