Presented By
O’Reilly + Cloudera
Make Data Work
29 April–2 May 2019
London, UK

Jerry Overton
Data Scientist, DXC Fellow, DXC


Jerry Overton is a data scientist and fellow in the Analytics Group and the global lead for artificial intelligence at DXC. Jerry is the author of Going Pro in Data Science: What It Takes to Succeed as a Professional Data Scientist from O’Reilly and teaches the live online training course Mastering Data Science at Enterprise Scale: How to Design and Implement Machine Learning Solutions That Improve Your Organization. In his blog, Doing Data Science, Jerry shares his experiences leading open research and transforming organizations using data science.


11:1511:55 Thursday, 2 May 2019
Data Science, Machine Learning & AI, Expo Hall
Location: Expo Hall (Capital Hall N24)
Secondary topics:  Ethics
Average rating: *****
(5.00, 3 ratings)
Machine learning (ML) algorithms are good at learning new behaviors but bad at identifying when those behaviors are harmful or don’t make sense. Bias, ethics, and fairness are big risk factors in ML. However, we creators have a lot of experience dealing with intelligent beings—one another. Jerry Overton uses this common sense to build a checklist for protecting against ethical violations with ML. Read more.