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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 …
Privacy And Legal Automation: The Dmca As A Case Study, Jonathon Penney
Privacy And Legal Automation: The Dmca As A Case Study, Jonathon Penney
Articles, Book Chapters, & Popular Press
Advances in artificial intelligence, machine learning, computing capacity, and big data analytics are creating exciting new possibilities for legal automation. At the same time, these changes pose serious risks for civil liberties and other societal interests. Yet, existing scholarship is narrow, leaving uncertainty on a range of issues, including a glaring lack of systematic empirical work as to how legal automation may impact people’s privacy and freedom. This article addresses this gap with an original empirical analysis of the Digital Millennium Copyright Act (DMCA), which today sits at the forefront of algorithmic law due to its automated enforcement of copyright …
Internet Surveillance, Regulation, And Chilling Effects Online: A Comparative Case Study, Jonathon Penney
Internet Surveillance, Regulation, And Chilling Effects Online: A Comparative Case Study, Jonathon Penney
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With internet regulation and censorship on the rise, states increasingly engaging in online surveillance, and state cyber-policing capabilities rapidly evolving globally, concerns about regulatory “chilling effects” online — the idea that laws, regulations, or state surveillance can deter people from exercising their freedoms or engaging in legal activities on the internet have taken on greater urgency and public importance. But just as notions of “chilling effects” are not new, neither is skepticism about their legal, theoretical, and empirical basis; in fact, the concept remains largely un-interrogated with significant gaps in understanding, particularly with respect to chilling effects online. This work …