Search Button
RSS icon Sort by:
Shapley Values – A Gentle Introduction
by Jo-fai Chow January 11, 2022 Data Science Shapley Technical Posts

If you can’t explain it to a six-year-old, you don’t understand it yourself. – Albert Einstein One fear caused by machine learning (ML) models is that they are blackboxes that cannot be explained. Some are so complex that no one, not even domain experts, can understand why they make certain decisions. This is of particular […]

Read More
5 Key Considerations for Machine Learning in Fair Lending
by Bruna Smith September 21, 2020 Financial Services Machine Learning Machine Learning Interpretability Responsible AI Shapley

This month, we hosted a virtual panel with industry leaders and explainable AI experts from Discover, BLDS, and H2O.ai to discuss the considerations in using machine learning to expand access to credit fairly and transparently and the challenges of governance and regulatory compliance. The event was moderated by Sri Ambati, Founder and CEO at H2O.ai. […]

Read More
From GLM to GBM – Part 2
by Patrick Moran July 9, 2020 Data Science Explainable AI GBM GLM Machine Learning Interpretability Responsible AI Shapley

How an Economics Nobel Prize could revolutionize insurance and lending Part 2: The Business Value of a Better Model Introduction In Part 1, we proposed better revenue and managing regulatory requirements with machine learning (ML). We made the first part of the argument by showing how gradient boosting machines (GBM), a type of ML, can […]

Read More
From GLM to GBM – Part 1
by Patrick Moran June 9, 2020 Data Science Explainable AI GBM GLM Machine Learning Interpretability Responsible AI Shapley

How an Economics Nobel Prize could revolutionize insurance and lending Part 1: A New Solution to an Old Problem Introduction Insurance and credit lending are highly regulated industries that have relied heavily on mathematical modeling for decades. In order to provide explainable results for their models, data scientists and statisticians in both industries relied heavily […]

Read More