Explaining Black Box Predictions with Sam Ritchie
EPISODE 73
|
NOVEMBER
25,
2017
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About this Episode
This week, we'll be featuring a series of shows recorded from Strange Loop, a great developer-focused conference that takes place every year right in my backyard! The conference is a multi-disciplinary melting pot of developers and thinkers across a variety of fields, and we're happy to be able to bring a bit of it to those of you who couldn't make it in person! In this episode, I speak with Sam Ritchie, a software engineer at Stripe. I caught up with Sam RIGHT after his talk at the conference, where he covered his team's work on explaining black box predictions.
In our conversation, we discuss how Stripe uses black box predictions for fraud detection, and he gives a few use case scenarios. We discuss Stripe's approach for explaining those predictions as well as other approaches, and briefly mention Carlos Guestrin's work on LIME paper, which he and I discuss in TWIML Talk #7.
About the Guest
Sam Ritchie
Mentat Collective
Resources
- Stripe
- Sam Presentation from Strange Loop 2017
- Slides from Sam's Presentation
- Get your free Nexosis API key here!
- "Mythos of Model Interpretability" by Zachary Lipton"
- Carlos Guestrin - LIME
- DARPA: Explainable AI
- DARPA: Explainable AI Slideshow
- Stuart Russell's Program on Human Compatible AI
- Interpretable ML Symposium
- AI transparency from the "robotics and artificial intelligence" report from the UK parliament, related to the GDPR
