Techniques for ML Model Transparency and Debugging


July 17, 2019


Gonzalo Ramos, Daniel S. Weld, Matthew Kay, Rich Caruana


Microsoft Research, University of Washington, University of Michigan, Microsoft Research


Without good models and the right tools to interpret them, data scientists risk making decisions based on hidden biases, spurious correlations, and false generalizations. This has led to a rallying cry for model interpretability. Yet the concept of interpretability remains nebulous, such that researchers and tool designers lack actionable guidelines for how to incorporate interpretability into models and accompanying tools. This panel brings together experts on visualization, machine learning and human interaction to present their views as well as discuss these complicated issues.