Machine learning and AI methods are increasingly being used in scientific contexts. While these techniques can often enable novel discoveries in powerful ways, they also have the potential to result in conclusions that are not reproducible or generalizable. This half-day symposium will bring together several speakers from diverse perspectives to discuss how machine learning and AI can endanger reproducibility and reduce bias, and how they can be used more effectively to power discoveries that will stand the test of time.