Summary
This project develops mathematical tools for assessing when artificial intelligence systems can be trusted after their predictions are used to make decisions. Modern AI tools help rank drug candidates, support medical decisions, screen large data sets, and suggest scientific hypotheses. In these settings, predictions are often used selectively and repeatedly: users may follow up only on top-ranked cases, choose confidence levels after seeing outputs, or let automated tools gather evidence over time. Standard uncertainty statements can become overly optimistic in such adaptive pipelines. This p
