Epsiode 93: Facial Recognition, Demographic Analysis & More with Timnit Gebru
Whether overt or unintentional, whether human- or technology-oriented, bias is something that every company must be vigilant about. And while it used to be something you might have to worry about with your employees, today it can be equally pervasive — and problematic — in the algorithms those employees create and the data they use.
Although the examples of bias in AI are numerous, one of the more prominent areas where we’ve seen it happen in recent years is in image processing. In this episode of the Impact Podcast, Jon Prial talks with Timnit Gebru a research scientist in the Ethical AI team at Google AI and a co-founder of the group Black in AI about some of the challenges with using facial recognition technology.
Who is Timnit Gebru?
Timnit Gebru is a research scientist in the Ethical AI team at Google AI. Prior to that, she was a postdoctoral researcher in the Fairness Accountability Transparency and Ethics (FATE) group at Microsoft Research, New York. She earned her PhD from 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 that arise as a result, including fine-grained image recognition, scalable annotation of images, and domain adaptation. She is currently studying the ethical considerations underlying any data mining project, and methods of auditing and mitigating bias in sociotechnical systems. The New York Times, MIT Tech Review, and others have recently covered her work. As a cofounder of the group Black in AI, she works to both increase diversity in the field and reduce the negative impacts of racial bias in training data used for human-centric machine learning models.