I will be speaking at the first one, introducing the basics of Machine Learning and scenarios in which its use can lead to inadvertent discrimination. My inimitable colleague Richard Berk (who actually builds models used to predict criminal recidivism used by the state of Pennsylvania) will be offering his comments following my talk.
The second seminar will provide a panel discussion on what the law demands in terms of "fair and equal treatment", and how it relates to the use of machine learning. The panelists will come from Philosophy, Political Science, and Law.
The third seminar will be given by our excellent Warren Center postdoc Jamie Morgenstern, and will focus on technical solutions to the problem of unfairness in machine learning, and how it can be squared with learning the optimal policy in online decision making settings.
Finally, the fourth seminar will be an exciting keynote delivered by the current Deputy U.S. CTO Ed Felten on uses of machine learning in government.
I believe the talks will be recorded.
We will begin next semester with the second Fels sponsored workshop, organized between computer science and economics -- a 2 day intensive workshop exploring current research on technical and economic solutions to addressing unfairness in decision making. More details to come.
Below is the schedule for this semester:
The “Optimizing Government” interdisciplinary research collaboration, supported by the Fels Policy Research Initiative, will hold the following workshops this fall:
: What is Machine Learning (and Why Might it be Unfair)?Fundamentals of machine learning with a focus on what makes it different from traditional statistical analysis and why it might lead to unfair outcomes.Speakers: Aaron Roth (Penn Computer Science), with comments from Richard Berk (Wharton Statistics; Chair of SAS Criminology)
: What Does Fair and Equal Treatment Demand?Current legal and moral norms about fairness and equal protection as they relate to the use of machine learning in government.Speakers: Panel featuring Samuel Freeman (Penn Philosophy), Nancy Hirschmann (Penn Political Science), and Seth Kreimer (Penn Law): Fairness and Performance Trade-Offs in Machine LearningTechnical solutions to fairness challenges raised by machine learning and their impacts on algorithm effectiveness.Speaker: Jamie Morgenstern (Penn Computer Science): Keynote on Machine Learning and GovernmentHow to use machine learning for a variety of administrative and policy functions, and findings from a White House initiative on artificial intelligence in government.Speaker: Ed Felten, Deputy U.S. CTO (Invited)
Each workshop will take place fromin Gittis 213 (Penn Law). You can enter the Law School through its main entrance at 3501 Sansom Street.