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Full-Text Articles in Law
Statistical Precedent: Allocating Judicial Attention, Ryan W. Copus
Statistical Precedent: Allocating Judicial Attention, Ryan W. Copus
Faculty Works
Suffering from a well-covered “crisis of volume,” the United States Courts of Appeals have patched together an ad hoc system of triage in an effort to provide cases with sufficient attention. For example, only some cases are assigned to central staff, analyzed by law clerks, orally argued, debated over by judges, or decided in published opinions. The courts have evaded overt disaster by increasing the number of active, senior, and visiting judges, but the additional personnel poses its own demands on attention—judges must also pay attention to one another in order to coherently develop and apply the law. With too …
Here There Be Dragons: The Likely Interaction Of Judges With The Artificial Intelligence Ecosystem, Fredric I. Lederer
Here There Be Dragons: The Likely Interaction Of Judges With The Artificial Intelligence Ecosystem, Fredric I. Lederer
Popular Media
No abstract provided.
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 …