An overview of responsible artificial intelligence
Who is this presentation for?Data scientists or analysts
Enabling responsible development of AI technologies is one of the field’s major challenges as it moves from research to practice. 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 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.” Overcoming these challenges and enabling responsible development is essential to ensure a future where AI and machine learning can be widely used.
Mehrnoosh Sameki and Sarah Bird examine six principles of development and deployment of trustworthy AI systems: four core principles of fairness, reliability and safety, privacy and security, and inclusiveness, underpinned by two foundational principles of transparency and accountability. You’ll learn how each principle plays a key role in responsible AI and what it means to take these principles from theory to practice. They cover open source products across different areas of the responsible AI umbrella—particularly transparency, fairness, and differential privacy—that aims to empower researchers, data scientists, and machine learning developers to take a significant step forward in this space, building trust between users and AI systems.
- A basic understanding of the machine learning lifecycle
What you'll learn
- Understand responsible AI principles, best practices, and open source tools around responsible development and deployment of AI systems
- Learn how to incorporate these tools and products in your machine learning lifecycle
Mehrnoosh Sameki (MERS)
Mehrnoosh Sameki is a technical program manager at Microsoft, responsible for leading the product efforts on machine learning interpretability within the Azure Machine Learning platform. Previously, she was a data scientist at Rue Gilt Groupe, incorporating data science and machine learning in the retail space to drive revenue and enhance customers’ personalized shopping experiences. She earned her PhD degree in computer science at Boston University.
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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