Tuesday, Sep 01, 2020. 12:00 PM. Link to Zoom for Online Seminar.

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Timnit Gebru -- Computer vision: who is harmed and who benefits?

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 have started to be used in all aspects of society. This last year has also seen a rise in public discourse regarding the use of computer-vision based technology by companies such as Google, Microsoft, Amazon, and IBM. In research, there exists work that purports to determine a person's sexuality from their social network profile images, or claims to classify "violent individuals" from drone footage.

On the other hand, recent works have shown that commercial gender classification systems have high disparities in error rates by skin-type and gender, the existence of the gender bias contained in current image captioning based works, and biases in the widely used CelebA dataset and proposes adversarial learning-based methods to mitigate its effects. Policymakers and other legislators have cited some of these seminal works in their calls to investigate the unregulated usage of computer vision systems. In this talk, I will highlight research on uncovering and mitigating issues of unfair bias and historical discrimination that trained machine learning models learn to mimic and propagate.

Bio: Timnit Gebru is a senior research scientist at Google co-leading the Ethical Artificial Intelligence research team. Her work focuses on mitigating the potential negative impacts of machine learning based systems. Timnit is also the co-founder of Black in AI, a non profit supporting Black researchers and practitioners in artificial intelligence. Prior to this, she did a postdoc at Microsoft Research, New York City in the FATE (Fairness Transparency Accountability and Ethics in AI) group, where she studied algorithmic bias and the ethical implications underlying any data mining project. She received her Ph.D. from the Stanford Artificial Intelligence Laboratory, studying computer vision under Prof. Fei-Fei Li. Her thesis pertains to data mining 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, she worked at Apple designing circuits and signal processing algorithms for various Apple products including the first iPad.