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Full-Text Articles in Engineering

Lowering Legal Barriers To Rpki Adoption, Christopher S. Yoo, David A. Wishnick Jan 2019

Lowering Legal Barriers To Rpki Adoption, Christopher S. Yoo, David A. Wishnick

Faculty Scholarship at Penn Law

Across the Internet, mistaken and malicious routing announcements impose significant costs on users and network operators. To make routing announcements more reliable and secure, Internet coordination bodies have encouraged network operators to adopt the Resource Public Key Infrastructure (“RPKI”) framework. Despite this encouragement, RPKI’s adoption rates are low, especially in North America.

This report presents the results of a year-long investigation into the hypothesis—widespread within the network operator community—that legal issues pose barriers to RPKI adoption and are one cause of the disparities between North America and other regions of the world. On the basis of interviews ...


Toward The Personalization Of Copyright Law, Adi Libson, Gideon Parchomovsky Jan 2019

Toward The Personalization Of Copyright Law, Adi Libson, Gideon Parchomovsky

Faculty Scholarship at Penn Law

In this Article, we provide a blueprint for personalizing copyright law in order to reduce the deadweight loss that stems from its universal application to all users, including those who would not have paid for it. We demonstrate how big data can help identify inframarginal users, who would not pay for copyrighted content, and we explain how copyright liability and remedies should be modified in such cases.


Transparency And Algorithmic Governance, Cary Coglianese, David Lehr Jan 2019

Transparency And Algorithmic Governance, Cary Coglianese, David Lehr

Faculty Scholarship at Penn Law

Machine-learning algorithms are improving and automating important functions in medicine, transportation, and business. Government officials have also started to take notice of the accuracy and speed that such algorithms provide, increasingly relying on them to aid with consequential public-sector functions, including tax administration, regulatory oversight, and benefits administration. Despite machine-learning algorithms’ superior predictive power over conventional analytic tools, algorithmic forecasts are difficult to understand and explain. Machine learning’s “black-box” nature has thus raised concern: Can algorithmic governance be squared with legal principles of governmental transparency? We analyze this question and conclude that machine-learning algorithms’ relative inscrutability does not pose ...