Derek Bambauer
Professor
- Gainesville FL UNITED STATES
- Levin College of Law
Derek Bambauer focuses on smart wills and AI, studying extensive international/comparative work on internet censorship.
Contact More Open optionsBiography
Derek Bambauer is the Irving Cypen Professor of Law at the Levin College of Law. A National Science Foundation-funded investigator, Derek’s research areas include artificial intelligence, cybersecurity, Internet censorship, and intellectual property. He maintains an active pro bono practice representing innovators, entrepreneurs, and security researchers.
Areas of Expertise
Social
Articles
Target(ed) Advertising
UC Davis Law ReviewDerek E. Bambauer
2024-04-13
Targeted advertising—using data about consumers to customize the ads they receive—is deeply controversial. It also creates a regulatory quandary. Targeted ads generate more money than untargeted ones for apps and online platforms. Apps and platforms depend on this revenue stream to offer free services to users, if not for their financial viability altogether.
AI, Artists, and Anti-Moral Rights
Georgetown Law JournalDerek E. Bambauer & Robert Woods
2024-04-10
Generative artificial intelligence (AI) tools are increasingly used to imitate the distinctive characteristics of famous artists, such as their voice, likeness, and style. In response, legislators have introduced bills in Congress that would confer moral rights protections, such as control over attribution and integrity, upon artists. This Essay argues such measures are almost certain to fail because of deep-seated, pervasive hostility to moral rights measures in U.S. intellectual property law.
Validity Assessment of Legal Will Statements as Natural Language Inference
ArXivAlice Saebom Kwak, et. al
2022-10-30
This work introduces a natural language inference (NLI) dataset that focuses on the validity of statements in legal wills. This dataset is unique because: (a) each entailment decision requires three inputs: the statement from the will, the law, and the conditions that hold at the time of the testator's death; and (b) the included texts are longer than the ones in current NLI datasets. We trained eight neural NLI models in this dataset.
