SC05: Toward Trustworthy Statistical Inference with Black-Box AI Predictions (Additional Fee)

The widespread adoption of AI and ML has reshaped modern data analysis. Predictions, embeddings, and synthetic data from black-box models, such as deep neural networks and large language models, are increasingly incorporated into downstream statistical workflows. For instance, predicted gene expression values or polygenic risk scores are often substituted for experimental assays, enabling researchers to enlarge cohorts and pursue hypotheses when direct measurement is infeasible, costly, or time-consuming. While AI/ML models can usually deliver strong predictive performance, their opaque mechanisms and potential biases introduce additional layers of uncertainty that can compromise the validity of classical inference. Treating black-box outputs as ground truth risks biased estimation, misleading confidence intervals, and invalid hypothesis tests.