Summary: As we have become ever more enamored with DNNs, and their accuracy and utility has been paced only by their complexity we will need to answer the question of whether we will ever really be able to explain what goes on inside.
Added by William Vorhies on May 11, 2020 at 2:41pm — No Comments
Summary: High stakes models like those that allocate scarce resources to competing hospitals are headline news. New thinking contrasting model-based versus model-free learning are emerging to describe new conditions we must consider before building or evaluating those models.
Added by William Vorhies on April 13, 2020 at 2:01pm — No Comments
Summary: GDPR carries many new data and privacy requirements including a “right to explanation”. On the surface this appears to be similar to US rules for regulated industries. We examine why this is actually a penalty and not a benefit for the individual and offer some insight into the actual wording of the GDPR regulation which also offers some relief.
Summary: The drive toward transparency and explainability in our modeling seems unstoppable. Up to now that meant sacrificing accuracy for interpretability. However, the ensemble method known as RuleFit may be the answer with both explainability and accuracy meeting or exceeding Random Forest.
If you’re like me and not doing modeling in a highly regulated industry like mortgage finance or insurance then when you produce a model, you are…Continue
Added by William Vorhies on June 27, 2017 at 10:02am — No Comments