Finding Common Rules to Wrangle Big Data
Scholar argues for unified rules of data governance to achieve fairness, efficiency, and stability.
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.
Regulating Automated Financial Advice
Scholars present framework for the regulation of automated advisors in the financial services industry.
Reporting Police Force in the Digital Age
Technology can help address the dearth of data surrounding police use of force.
The FTC Will Not Leave Companies to Their Own Devices
The FTC recently issued regulatory recommendations for the growing platform of devices in the Internet of Things.
The Costs and Benefits of Data Brokers
Data brokers’ use of big data leads to questions about how best to protect consumers.