Sep 9–12, 2019

Developing AI responsibly

Sarah Bird (Microsoft)
9:35am9:50am Wednesday, September 11, 2019
Location: Hall 2
Secondary topics:  Ethics, Security, and Privacy
Average rating: ****.
(4.00, 8 ratings)

Researchers and practitioners from different disciplines have highlighted the ethical and legal challenges posed by the use of machine learning in many current and future real-world applications. Now there are calls from across the industry (academia, government, and industry leaders) for technology creators to ensure that AI is used only in ways that benefit people and to engineer responsibility into the very fabric of the technology. Doing this effectively will require innovations across the board in AI technology, tools, and processes. Sarah Bird outlines her perspective on some of the major challenges in responsible AI development and examines promising new tools and technologies to help enable it in practice.

What you'll learn

  • Discover some of the major challenges in responsible AI development
  • Learn new tools and techniques to enable responsible AI
Photo of Sarah Bird

Sarah Bird

Microsoft

Sarah Bird is a principle program manager at Microsoft where she leads research and emerging technology strategy for Azure AI. Sarah works to accelerate the adoption and impact of AI by bringing together the latest innovations research with the best of open source and product expertise to create new tools and technologies. She leads the development of responsible AI tools in Azure Machine Learning. She’s also an active member of the Microsoft Aether committee, where she works to develop and drive company-wide adoption of responsible AI principles, best practices, and technologies. Previously, Sarah was one of the founding researchers in the Microsoft FATE research group and worked on AI fairness in Facebook. She’s an active contributor to the open source ecosystem; she cofounded ONNX, an open source standard for machine learning models and was a leader in the PyTorch 1.0 project. She was an early member of the machine learning systems research community and has been active in growing and forming the community. She cofounded the SysML research conference and the Learning Systems workshops. She holds a PhD in computer science from the University of California, Berkeley, advised by Dave Patterson, Krste Asanovic, and Burton Smith.

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