Timnit Gebru

Research Scientist at Google, Ethical AI Team

Computer Vision: Who benefits and who is harmed?

January 20, 2021 - 12:00 PM Eastern Time

Talk Abstract:

Computer vision has ceased to be a purely academic endeavor. From law enforcement, to border control, to employment, healthcare diagnostics, and assigning trust scores, computer vision systems are being rapidly integrated into all aspects of society. In research, there are works that purport to determine a person’s sexuality from their social network profile images, others that claim to classify “violent individuals” from drone footage. These works were published in high impact journals, and some were presented at workshops in top tier computer vision conferences such as CVPR.
A critical public discourse surrounding the use of computer-vision based technologies has also been mounting. For example, the use of facial recognition technologies by policing agencies has been heavily critiqued and, in response, companies such as Microsoft, Amazon, and IBM have pulled or paused their facial recognition software services. Gender Shades showed that commercial gender classification systems have high disparities in error rates by skin-type and gender, and other works discuss the harms caused by the mere existence of automatic gender recognition systems. Recent papers have also exposed shockingly racist and sexist labels in popular computer vision datasets--resulting in the removal of some. In this talk, I will highlight some of these issues and proposed solutions to mitigate bias, as well as how some of the proposed fixes could exacerbate the problem rather than mitigate it.

Speaker Bio:

I am currently a research scientist at Google in the ethical AI team. Prior to that I did a postdoc at Microsoft Research, New York City in the FATE (Fairness Transparency Accountability and Ethics in AI) group, where I studied algorithmic bias and the ethical implications underlying projects aiming to gain insights from data (see this New York Times article for an example of my work). I received my PhD from the Stanford Artificial Intelligence Laboratory, studying computer vision under Fei-Fei Li. My thesis pertains to using large scale publicly available images to gain sociological insight, and working on computer vision problems that arise as a result. The Economist, The New York Times and others have covered part of this work. Prior to joining Fei-Fei's lab I worked at Apple designing circuits 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). My research was supported by the NSF foundation GRFP fellowship and the Stanford DARE fellowship