Media Summary: So you've built a model. It's deployed. Now what? How do you know if it's performing well? How do you keep track of predictions? PyCon Belarus 2019, Minsk Data Science Track A practical guide towards In the first segment of the workshop, Professor Hima Lakkaraju motivates the need for interpretable
Machine Learning Explainability Bias Detection - Detailed Analysis & Overview
So you've built a model. It's deployed. Now what? How do you know if it's performing well? How do you keep track of predictions? PyCon Belarus 2019, Minsk Data Science Track A practical guide towards In the first segment of the workshop, Professor Hima Lakkaraju motivates the need for interpretable Interpretable models can be understood by a human without any other aids/techniques. On the other hand, In this video, I'm demonstrating a project I worked on called the Hiring Explainable AI for bias detection in healthcare.
