Top 10 ways your Machine Learning models may have leakage

Top 10 ways your Machine Learning models may have leakage Rayid Ghani, Joe Walsh, Joan Wang If you’ve ever worked on a real-world machine learning problem, you’ve probably introduced (and hopefully discovered and fixed) leakage into your system at some point. Leakage is when your model has access to data at training/building time that it [...]

By |2020-03-11T01:01:34+00:00Jan 24, 2020|0 Comments

Why what Cambridge Analytica did was Unacceptable

Why what Cambridge Analytica did was Unacceptable And how we can future-proof against it The last few days, we’ve all been hearing about Cambridge Analytica, the Trump Campaign, and their use of Facebook data in the 2016 campaign. Some of you have probably also heard that 1) this use of Facebook data is not new, [...]

By |2020-03-10T21:02:13+00:00Mar 21, 2018|1 Comment

You Say You Want Transparency and Interpretability?

You Say You Want Transparency and Interpretability? We keep hearing and saying that in order to implement and correctly use machine learning and predictive models, they must be transparent and interpretable. That makes sense. You don’t want a black box model making important decisions — although one could argue that the guts and intuitions of many human beings [...]

By |2020-03-10T20:51:56+00:00Apr 29, 2016|0 Comments

One-to-One (Personalized) Public Policy

One-to-One (Personalized) Public Policy One of the primary reasons I joined University of Chicago was the chance to be at the intersection of Computation, Data, and Policy, and work on large-scale social problems that lead to an impact on public policy. I’m often asked how Data/Analytics/Machine Learning can practically impact any kind of policy decisions [...]

By |2020-03-10T21:08:05+00:00Aug 22, 2013|0 Comments