Peter Hersh, JMP Senior Systems Engineer, SAS
Florian Vogt, Systems Engineer, JMP
Russ Wolfinger, Director of Scientific Discovery and Genomics, JMP
Laura Lancaster, Principal Research Statistician Developer, JMP
Artificial intelligence algorithms are useful for gaining insight into complex problems. One of the drawbacks of these algorithms is they are often difficult to interpret. The lack of interpretability can make models generated using these AI algorithms less trustworthy and less useful. This talk will show how utilizing a few features in JMP can make AI more understandable. The presentation will feature performing “what if” hypothesis testing using the prediction profiler, testing for model robustness utilizing Monte Carlo Simulations, and analyzing Shapley values, a new feature in JMP Pro 17, to explore contrastive explanations.
