Apply fair and private models, white-hat and forensic model debugging, and common sense to protect machine learning models from malicious actors.
Patrick Hall is senior director for data science products at H2O.ai where he focuses mainly on model interpretability and model management. Patrick is also currently an adjunct professor in the Department of Decision Sciences at George Washington University, where he teaches graduate classes in data mining and machine learning. Prior to joining H2O.ai, Patrick held global customer facing roles and research and development roles at SAS Institute.
The importance of testing your tools, using multiple tools, and seeking consistency across various interpretability techniques.
Mix-and-match approaches for visualizing data and interpreting machine learning models and results.
Measure your model’s business impact, not just its accuracy.
Tips for using machine learning models in regulated industries.