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Full-Text Articles in Law and Society
Legal Risks Of Adversarial Machine Learning Research, Ram Shankar Siva Kumar, Jonathon Penney, Bruce Schneier, Kendra Albert
Legal Risks Of Adversarial Machine Learning Research, Ram Shankar Siva Kumar, Jonathon Penney, Bruce Schneier, Kendra Albert
Articles, Book Chapters, & Popular Press
Adversarial machine learning is the systematic study of how motivated adversaries can compromise the confidentiality, integrity, and availability of machine learning (ML) systems through targeted or blanket attacks. The problem of attacking ML systems is so prevalent that CERT, the federally funded research and development center tasked with studying attacks, issued a broad vulnerability note on how most ML classifiers are vulnerable to adversarial manipulation. Google, IBM, Facebook, and Microsoft have committed to investing in securing machine learning systems. The US and EU are likewise putting security and safety of AI systems as a top priority.
Now, research on adversarial …
Can Cyber Harassment Laws Encourage Online Speech?, Jonathon Penney
Can Cyber Harassment Laws Encourage Online Speech?, Jonathon Penney
Articles, Book Chapters, & Popular Press
Do laws criminalizing online harassment and cyberbullying "chill" online speech? Critics often argue that they do. However, this article discusses findings from a new empirical legal study that suggests, counter-intuitively, that while such legal interventions likely have some dampening effect, they may also facilitate and encourage more speech, expression, and sharing by those who are most often the targets of online harassment: women. Relevant findings on this point from this first-of-its-kind study are set out and discussed along with their implications.
Chilling Effects: Online Surveillance And Wikipedia Use, Jonathon Penney
Chilling Effects: Online Surveillance And Wikipedia Use, Jonathon Penney
Articles, Book Chapters, & Popular Press
This article discusses the results of the first empirical study providing evidence of regulatory “chilling effects” of Wikipedia users associated with online government surveillance. The study explores how traffic to Wikipedia articles on topics that raise privacy concerns for Wikipedia users decreased after the widespread publicity about NSA/PRISM surveillance revelations in June 2013. Using an interdisciplinary research design, the study tests the hypothesis, based on chilling effects theory, that traffic to privacy-sensitive Wikipedia articles reduced after the mass surveillance revelations. The Article finds not only a statistically significant immediate decline in traffic for these Wikipedia articles after June 2013, but …