Bio: Timnit Gebru is a computer scientist whose work explores algorithmic bias and the ethical implications of data mining projects.

Timnit is an advocate for diversity in technology and is the cofounder of Black in AI, a community of black researchers working in artificial intelligence. She seeks both to increase diversity in the field of AI and to reduce the negative impacts of racial bias in training data used for human-centric machine learning models.

Timnit previously worked with the Ethical AI team at Google AI and conducted postdoctoral research in the Fairness Accountability Transparency and Ethics (FATE) group at Microsoft Research. She has also worked at Apple, where she helped develop signal-processing algorithms for the first iPad.

Timnit was born in Ethiopia and immigrated to the United States when she was 15. She studied at Stanford University, where she earned her B.S. and M.S. in electrical engineering, as well as a PhD from the Stanford Artificial Intelligence Laboratory, where she studied computer vision under Fei-Fei Li.

Part of the CDAC Winter 2021 Distinguished Speaker Series:

Bias Correction: Solutions for Socially Responsible Data Science

Security, privacy and bias in the context of machine learning are often treated as binary issues, where an algorithm is either biased or fair, ethical or unjust. In reality, there is a tradeoff between using technology and opening up new privacy and security risks. Researchers are developing innovative tools that navigate these tradeoffs by applying advances in machine learning to societal issues without exacerbating bias or endangering privacy and security. The CDAC Winter 2021 Distinguished Speaker Series will host interdisciplinary researchers and thinkers exploring methods and applications that protect user privacy, prevent malicious use, and avoid deepening societal inequities — while diving into the human values and decisions that underpin these approaches.