Daring to DAIR: Distributed AI Research with Timnit Gebru - #568
Today we’re joined by friend of the show Timnit Gebru, the founder and executive director of DAIR, the Distributed Artificial Intelligence Research Institute. In our conversation with Timnit, we discuss her journey to create DAIR, their goals and some of the challenges shes faced along the way. We start is the obvious place, Timnit being “resignated” from Google after writing and publishing a paper detailing the dangers of large language models, the fallout from that paper and her firing, and the eventual founding of DAIR. We discuss the importance of the “distributed” nature of the institute, how they’re going about figuring out what is in scope and out of scope for the institute’s research charter, and what building an institution means to her. We also explore the importance of independent alternatives to traditional research structures, if we should be pessimistic about the impact of internal ethics and responsible AI teams in industry due to the overwhelming power they wield, examples she looks to of what not to do when building out the institute, and much much more!
The complete show notes for this episode can be found at twimlai.com/go/568
Trends in Fairness and AI Ethics with Timnit Gebru - #336
Today we keep the 2019 AI Rewind series rolling with friend-of-the-show Timnit Gebru, a research scientist on the Ethical AI team at Google. A few weeks ago at NeurIPS, Timnit joined us to discuss the ethics and fairness landscape in 2019. In our conversation, we discuss diversification of NeurIPS, with groups like Black in AI, WiML and others taking huge steps forward, trends in the fairness community, quite a few papers, and much more.
Ep. 44: Forget Polls, Here's What Street View, and AI, Can Tell You About How People Will Vote
Election polling is an inexact science. If you've been paying attention to American politics at all over the past year or two, you don't need us to tell you that. But what if instead of asking voters their opinions on the candidates or the issues you took a different approach, one that involves artificial intelligence... and cars. Joining us for this edition of the AI podcast is Timnit Gebru, a post-doctoral researcher at Microsoft Research in New York and a newly minted PhD from the Stanford Artificial Intelligence Laboratory. Timnit is co-author of a paper titled "Using Deep learning and Street View to Estimate the Demographic Makeup of Neighborhoods Across the United States."
Using Deep Learning and Google Street View to Estimate Demographics with Timnit Gebru
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. The notes for this show can be found at twimlai.com/talk/88 For series information, visit twimlai.com/nips2017
