Metro21 Lunch and Learn: Public Policy Analytics: Code and Context for Data Science in Government
On Monday, May 3, Dr. Ken Steif presented his book, "Public Policy Analytics: Code & Context for Data Science in Government."
How do algorithms in government differ from business? In business, revenue is the only relevant bottom line, but in government algorithms must optimize for several disparate bottom lines like equity, fairness, bureaucracy, politics and more. In his new book, 'Public Policy Analytics: Code & Context for Data Science in Government', Dr. Ken Steif presents both code examples and an analytical framework for developing algorithms to meet these requirements. In this talk, he will discuss why data science and Planning are one in the same; how certain machine learning algorithms can help governments better allocate their limited resources; and how 'Algorithmic Governance' can help agencies develop data science tools that are both useful and fair. Take a look at the open source version of the book here.
Dr. Ken Steif has been at the forefront of data driven public policy for more than fifteen years. He combines technical knowledge of Geographic Information Systems and applied statistics with an interest in housing policy, child welfare, education, the economics of neighborhood change, transportation policy and more. He is the Director of the Master of Urban Spatial Analytics program at the University of Pennsylvania and runs a consultancy, Urban Spatial, at the intersection of data science and public policy.
View Ken's full presentation here.