Using Deep Learning and Google Street View to Estimate Demographics with Timnit Gebru

800 800 The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

This week on the podcast we’re featuring a series of conversations from the NIPs conference in Long Beach, California. I attended a bunch of talks and learned a ton, organized an impromptu roundtable on Building AI Products, and met a bunch of great people, including some former TWIML Talk guests. In this episode I sit down with Timnit Gebru, postdoctoral researcher at Microsoft Research in the Fairness, Accountability, Transparency and Ethics in AI, or FATE, group. Timnit is also one of the organizers behind the Black in AI group, which held a very interesting symposium and poster session at NIPS. I’ll link to the group’s page in the show notes.

I’ve been following Timnit’s work for a while now and was really excited to get a chance to sit down with her and pick her brain. We packed a ton into this conversation, especially keying in on her recently released paper “Using Deep Learning and Google Street View to Estimate the Demographic Makeup of the US”. Timnit describes the pipeline she developed for this research, and some of the challenges she faced building and end-to-end model based on google street view images, census data and commercial car vendor data. We also discuss the role of social awareness in her work, including an explanation of how domain adaptation and fairness are related and her view of the major research directions in the domain of fairness.

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Thanks to our Sponsor

Intel Nervana LogoI’d like to thank our friends over at Intel Nervana for their sponsorship of this podcast and our NIPS series. While Intel was very active at NIPS, with a bunch of workshops, demonstrations and poster sessions, their big news at NIPS was the first public viewing of the Intel Nervana™ Neural Network Processor, or NNP. The goal of the NNP architecture is to provide the flexibility needed to support deep learning primitives while making the core hardware components as efficient as possible, giving neural network designers powerful tools for solving larger and more difficult problems while minimizing data movement and maximizing data re-use. To learn more about Intel’s AI Products Group and the Intel Nervana NNP, visit

About Timnit

Mentioned in the Interview


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