PPR

Ten Years of Regulatory Guidance

Ten Years of Regulatory Guidance

The students who have built The Regulatory Review inspire hope and offer reason to celebrate even amid challenging times.

Reflecting on a Decade of The Regulatory Review

Reflecting on a Decade of The Regulatory Review

The Review’s Editors-in-Chief reflect on their experiences and celebrate what makes this publication so extraordinary.

Celebrating The Regulatory Review’s Tenth Anniversary

Celebrating The Regulatory Review’s Tenth Anniversary

To commemorate The Regulatory Review’s 10th anniversary, leading scholars and practitioners reflect on regulation’s past, present, and future.

What the Shutdown Revealed About the Value of Public Service

What the Shutdown Revealed About the Value of Public Service

In his 2019 Distinguished Regulation Lecture at Penn Law, Paul C. Light shares a message of hope about public service.

Improving Regulatory Decisions

Improving Regulatory Decisions

Leading business, law, and government professionals study regulation in cutting-edge executive education course.

How Machine Learning Can Improve Public Sector Services

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?

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?

Should Robots Make Law?

Workshop evaluated benefits and challenges of delegating government decision-making to computers.

Regulating the Robots that Help Us Decide

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 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

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

The Usefulness—and Possible Dangers—of Machine Learning

University of Pennsylvania workshop addresses potential biases in the predictive technique.