The rapid advancement of generative AI has brought forth incredible opportunities to transform industries and solve complex problems. However, ensuring the responsible development and deployment of these technologies is crucial to harness their potential effectively. In this talk, I will delve into Microsoft's journey of creating and utilizing generative AI applications, such as GitHub Copilot and Bing Chat, while highlighting the responsible AI foundation in Azure AI that powers them.
This responsible AI foundation in Azure AI not only drives our own applications but also equips others with the necessary tools and resources to build ethical AI solutions. I will showcase examples of how developers can leverage this foundation to create responsible AI systems.
Join us for an engaging conversation on the importance of responsible AI and how we can work together to create a future where AI benefits everyone.
Speaker
Sarah Bird
Responsible AI Lead, Azure AI @Microsoft
Sarah leads Responsible AI for foundational AI technologies at Microsoft. Sarah works to accelerate the adoption and positive impact of AI by bringing together the latest innovations in research, product, and policy to enable the use of new AI technologies such as generative AI models. As an expert in responsible AI implementation, she contributes to the development and adoption of responsible AI principles, best practices, and technologies company-wide. Sarah led the cross-company team of experts to develop the new Bing responsibly and led the Responsible AI development for Github Copilot. Sarah led the product development of responsible AI tools including Fairlearn, SmartNoise and InterpretML. She is an active member of the Microsoft AETHER committee and contributed to the creation of the Microsoft Responsible AI Standard. Sarah was one of the founding researchers in the Microsoft FATE research group. Sarah is active contributor to the open source ecosystem, she co-founded ONNX, Fairlearn and OpenDP’s SmartNoise and was a leader in the Pytorch 1.0 and InterpretML projects. She was an early member of the machine learning systems research community and has been active in growing and forming the community. She co-founded the MLSys research conference and the Learning Systems workshops.
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