Free
*Stanford is taking several precautions on campus to inhibit the potential spread of the COVID-19 (novel coronavirus). As a result of those precautions, we regret to inform everyone that in-person attendance at the final two AI for Good sessions will be limited to Stanford students, faculty, and staff.*
AI for Everyone | A Multi-Disciplinary Approach
How do we ensure AI solutions are designed to work for all – regardless of race, gender, ability, or background? Within the promise of artificial intelligence lie a number of difficult questions and challenges. A multi-disciplinary approach, one that has people from a variety of backgrounds involved in designing the solutions, is needed. In their talks and joint Q&A, Timnit and Omer will address challenges around data collection and algorithm development regarding bias, fairness, accountability, differential privacy and ethics.
Speakers:
Timnit Gebru, Research Scientist and Technical Co-lead of Google’s Ethical Artificial Intelligence TeamOmer Reingold, The Rajeev Motwani Professor of Computer Science at Stanford University
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.
Omer Reingold
Omer Reingold is the Rajeev Motwani professor of computer science at Stanford University. Past positions include Samsung Research America, the Weizmann Institute of Science, Microsoft Research, the Institute for Advanced Study in Princeton, NJ and AT&T Labs. His research is in the foundations of computer science and most notably in computational complexity, cryptography and the societal impact of computation. He is an ACM Fellow and among his distinctions are the 2005 Grace Murray Hopper Award and the 2009 Gödel Prize.
Livestream this event here: https://livestream.com/accounts/1973198/AI-For-Good
