Administrative Law

Top Regulatory Essays of 2017

Top Regulatory Essays of 2017

The Regulatory Review highlights our top essays written by outside contributors in 2017.

The 2017 Regulatory Year in Review

The 2017 Regulatory Year in Review

The Regulatory Review celebrates its most widely read work published on the year 2017’s regulatory developments.

Of “Workarounds” and Bureaucrats

Of “Workarounds” and Bureaucrats

Civil service reformers should consider changes to lengthy, single-agency employee tenures.

Getting Back to the Basics with Agency Rulemaking

Getting Back to the Basics with Agency Rulemaking

The United States needs a bipartisan push to bring transparency and accountability back into the rulemaking process.

Repealing the CFPB’s Arbitration Rule

Repealing the CFPB’s Arbitration Rule

President Trump signs measure rescinding the financial consumer watchdog’s recent rule.

Does the Administrative State Threaten U.S. Democracy?

Does the Administrative State Threaten U.S. Democracy?

Panel focuses on claims of potential dangers from growth in government agencies.

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.