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Practical Lessons and Challenges in Building Fair and Equitable AI/ML Systems

Dr. Rayid Ghani
When: April 11, 2022 @ 11:00am - 12:00pm
Location: Register for the Zoom webinar here:
Audiences: All are welcome to attend
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This lecture satisfies requirements for CSCI 591: Research Colloquium.


As organizations become more aware of the need to build ML/AI systems that result in fair and equitable outcomes, they have started to struggle with operationalizing that need. In this talk, I’ll discuss lessons learned over the past few years working with various government agencies and non-profits across health, criminal justice, social services, education, and economic & workforce development on how those organizations view this challenge, how they’re attempting to design ML/AI systems, and what gaps exist in the work that Fair ML researchers have been producing. I’ll also discuss some examples of methods and tools that were useful in those collaborations and resulted in more equitable impact through the use of ML.


Rayid Ghani is a Professor in Machine Learning and Public Policy at Carnegie Mellon University focused on developing and using AI/Machine Learning/Data Science to help tackle large public policy and societal challenges in a fair and equitable manner. Among other areas, Rayid works with governments and non-profits in policy areas such as health, criminal justice, education, public safety, economic development, and urban infrastructure. Before joining Carnegie Mellon University, Rayid was the Founding Director of the Center for Data Science & Public Policy, Research Associate Professor in Computer Science, and a Senior Fellow at the Harris School of Public Policy at the University of Chicago. Previously, Rayid was the Chief Scientist of the Obama 2012 Election Campaign.

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