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.
Cost-Benefit Analysis and Reasoned Agency Decision-Making
Scalia recounts his role in challenging a SEC rule on behalf of the U.S. Chamber of Commerce.
The Value of Public Participation in Rulemaking
Appearing before agencies affords the public an important opportunity to have a genuine impact on the law.
The Public’s Role in Administrative Law
Public participation in the rulemaking process serves a vital role in improving the law.