The Regulatory Review’s Series from 2017
The Regulatory Review highlights the series published in 2017.
Top Regulatory Stories of 2017
The Regulatory Review highlights the top regulatory stories written by our staff in 2017.
Top Regulatory Essays of 2017
The Regulatory Review highlights our top essays written by outside contributors in 2017.
How Machine Learning Can Improve Public Sector Services
Experts explain how algorithms can aid government health and welfare work.
How Can We Reveal Bias in Computer Algorithms?
A legal scholar and a computer scientist explored how to limit machine learning biases.
Should Robots Make Law?
Workshop evaluated benefits and challenges of delegating government decision-making to computers.
Regulating the Robots that Help Us Decide
Professors tackle the challenges of regulating financial robo advisors.
Experts Weigh in on Fairness and Performance Trade-Offs in Machine Learning
Experts from multiple disciplines discuss notions of fairness within the age of machine learning.
Machine Learning’s Implications for Fairness and Justice
Penn professors grapple with balancing efficiency and equality of government algorithms.
The Usefulness—and Possible Dangers—of Machine Learning
University of Pennsylvania workshop addresses potential biases in the predictive technique.
Optimizing Government
The Optimizing Government Project brings together scholars and researchers to discuss the use of machine learning by government.
Concluding Thoughts on Cost-Benefit Analysis and the Public’s Impact in Rulemaking
The Supreme Court’s decision in Michigan v. EPA illustrates that the public has a serious role to play in the rulemaking process.