Cornell Law Review

Article

Automated Legal Guidance

Joshua D. Blank, Professor of Law and Faculty Director of Strategic Initiatives, University of California, Irvine School of Law

Leigh Osofsky, Professor of Law, University of North Carolina School of Law

This Article offers one of the first critiques of these new systems of artificial intelligence. It shows that automated legal guidance currently relies upon the concept of “simplexity,” whereby complex law is presented as though it is simple, without actually engaging in simplification of the underlying law. While this approach offers potential gains in terms of efficiency and ease of use, it also causes the government to present the law as simpler than it is, leading to less precise advice and potentially inaccurate legal positions. Using the Interactive Tax Assistant as a case study, the Article shows that the use of simplexity in automated legal guidance is more powerful and pervasive than in static publications because it is personalized, non-qualified, and instantaneous. Further, it argues that understanding the costs as well as the benefits of current forms of automated legal guidance is essential to evaluating even more sophisticated, but also more opaque, automated systems that governments are likely to adopt in the future.

Dec 2020

Recent News & Events

Professor Katyal’s Cornell Article is judged as best 2019 intellectual property law review article

Professor Sonia Katyal’s Article The Paradox of Source Code Secrecy was selected for inclusion in the 2020 edition of the Intellectual Property Law Review, an anthology published annually by Thomson Reuters (West). This article was originally published in 104 Cornell L. Rev. 1183 (2019). Abstract In Lear v. Adkins, the Supreme Court precipitously wrote, “federal…

Jul 2020

Article

You Might Be a Robot

Bryan Casey & Mark A. Lemley

As robots and artificial intelligence (AI) increase their influence over society, policymakers are increasingly regulating them. But to regulate these technologies, we first need to know what they are. And here we come to a problem. No one has been able to offer a decent definition of robots and AI—not even experts. What’s more, technological…

Jan 2020

Article

The Paradox of Source Code Secrecy

Sonia K. Katyal, Haas Distinguished Chair and Chancellor’s Professor of Law, University of California at Berkeley.

Today, the government relies on machine learning and AI in predictive policing analysis, family court delinquency proceedings, parole decisions, and DNA and forensic science techniques, among other areas, producing a fundamental conflict between civil rights and automated decisionmaking. Ground zero for this conflict, I argue, has become the murky, messy intersection between software, trade secrecy, and public governance. In many…

Jul 2019