26 min listen
Explaining Black Box Predictions with Sam Ritchie - TWiML Talk #73
FromThe TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
Explaining Black Box Predictions with Sam Ritchie - TWiML Talk #73
FromThe TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
ratings:
Length:
38 minutes
Released:
Nov 25, 2017
Format:
Podcast episode
Description
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. The notes for this show can be found at twimlai.com/talk/73 For more series info, visit twimlai.com/STLoop
Released:
Nov 25, 2017
Format:
Podcast episode
Titles in the series (100)
This Week in ML & AI - 6/24/16: Dueling Neural Networks at ICML, Plus Training a Robotic Housekeeper: This Week in Machine Learning & AI brings you the… by The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)