Understanding the Limits of AI: When Algorithms Fail

Timnit Gebru, Microsoft Research

Timnit Gebru explores the social implications of algorithmic bias.

Timnit Gebru, Postdoctoral Researcher, Microsoft Research

Timnit Gebru works in the Fairness, Accountability, Transparency, and Ethics (FATE) group at Microsoft Research. Before joining Microsoft, she was a PhD student in the Stanford Artificial Intelligence Laboratory, studying computer vision under Fei-Fei Li. Her main research interest is in data-mining large-scale, publicly available images to gain sociological insight, and working on computer-vision problems such as fine-grained image recognition, scalable annotation of images, and domain adaptation. The Economist, the New York Times, and others have recently covered part of this work. She is currently studying how to take data-set bias into account while designing machine-learning algorithms and examining the ethical considerations underlying any data-mining project. As a cofounder of the group Black in AI, she works to both increase diversity in the field and reduce the impact of racial bias in the data.

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Intelligent Machines
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