Causal Discovery Algorithms Tested for Legal Argument Generation
A new paper on arXiv explores whether causal discovery algorithms, pioneered by Judea Pearl, can be applied to law for automated generation of legal arguments. Pearl received the 2011 Turing Award for his work on causal reasoning. These algorithms analyze multivariate datasets to infer causal relationships, widely used in medicine and economics but not yet in law. The researchers created a novel legal dataset identifying 17 legal concepts (e.g., physical assault, property dispute) and annotated 150 homicide cases. The study investigates if these algorithms can help generate legal arguments, potentially bridging a gap in AI and law.
Key facts
- Judea Pearl received the 2011 Turing Award for causal reasoning.
- Causal discovery algorithms automatically find causal relationships in data.
- These algorithms are used in medicine and economics but not law.
- The paper investigates using them for legal argument generation.
- A dataset of 17 legal concepts and 150 homicide cases was created.
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- arXiv