It has become increasingly common for a reader to follow a URL cited in a court opinion or a law review article, only to be met with an error message because the resource has been moved from its original online address. This form of reference rot, commonly referred to as 'linkrot', has arisen from the disconnect between the transience of online materials and the permanence of legal citation, and will only become more prevalent as scholarly materials move online. The present paper*, written by Jonathan Zittrain, Kendra Albert and Lawrence Lessig, explores the pervasiveness of linkrot in academic and legal citations, finding that more than 70% of the URLs within the Harvard Law Review and other journals, and 50% of the URLs within United States Supreme Court opinions, do not link to the originally cited information. In light of these results, a solution is proposed for authors and editors of new scholarship that involves libraries undertaking the distributed, long-term preservation of link contents.
In addition to their security properties, adversarial machine-learning attacks and defenses have political dimensions. They enable or foreclose certain options for both the subjects of the machine learning systems and for those who deploy them, creating risks for civil liberties and human rights. In this paper, we draw on insights from science and technology studies, anthropology, and human rights literature, to inform how defenses against adversarial attacks can be used to suppress dissent and limit attempts to investigate machine learning systems, using facial recognition technology as a case study. To make this concrete, we use real-world examples of how attacks such as perturbation, model inversion, or membership inference can be used for socially desirable ends. Although this analysis' predictions may seem dire, there is hope. Efforts to address human rights concerns in the commercial spyware industry provide guidance for similar measures to ensure ML systems serve democratic, not authoritarian ends. * Authors ordered alphabetically
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