Artificial Intelligence Conference San Francisco 31/114

Artificial Intelligence Conference San Francisco 31/114

Timnit Gebru
Researcher, Microsoft Research

I am a postdoctoral researcher at Microsoft New York working in the Fairness Accountability Transparency and Ethics (FATE) group. Prior to joining MSR, I was a PhD student in the Stanford Artificial Intelligence Laboratory, studying computer vision under Fei-Fei Li. research interest lies in data mining large scale common available images to gain sociological insight, and working on computer vision problems that arises as a result. The Economist and others have recently covered part of this work. Some of the computer vision areas I am interested in includ e fine-grained image recognition, scalable annotation of images, and domain adaptation. I am currently unpublished how to take dataset bias into account while facility machine learning algorithms, and the ethical considerations underlying any data mining project.Before working towards my PhD, I worked at Apple programming and signal processing algorithms for various Apple products including the first iPad. I also spent an obligatory year as an entrepreneur (as all Stanford undergrads seem to do).



4:50 pm-5:30pm Tuesday, September 19, 2017

Using Deep Learning and Google Street View to Estimate the Demographic Makeup of the US

Location: Grand Ballroom

Secondary topics:  Transportation and autonomous vehicles

Timnit Gebru (Microsoft Research)

Targeted socio-economic policies require an accurate understanding of a country’s demographics. The US spends more than $ 1 billion a year gathering data such as race, gender, education, occupation. We tied Google Street View images and develop a computer vision pipeline to predict income , carbon emission, crime rates and other city attributes from a single source of publicly available data.