Abstract
Legal judgment prediction encompasses the automated prediction of case outcomes by leveraging historical facts and opinions. While this approach holds the potential to enhance the efficiency of the legal system, it also raises critical concerns regarding the perpetuation of biases. Abstract Meaning Representation has shown promise as an intermediate text representation in various downstream NLP tasks due to its ability to capture semantically meaningful information in a graph-like structure. In this paper, we employ this ability of AMR in the legal judgement prediction task and assess to what extent it encodes biases, or conversely, abstracts away from them. Our study reveals that while AMR-based models exhibit worse overall performance than transformer-based models, they are less biased for attributes like age and defendant state compared to gender. By shedding light on these findings, this paper contributes to a more nuanced understanding of AMR’s potential benefits and limitations in legal NLP.- Anthology ID:
- 2024.insights-1.13
- Volume:
- Proceedings of the Fifth Workshop on Insights from Negative Results in NLP
- Month:
- June
- Year:
- 2024
- Address:
- Mexico City, Mexico
- Editors:
- Shabnam Tafreshi, Arjun Akula, João Sedoc, Aleksandr Drozd, Anna Rogers, Anna Rumshisky
- Venues:
- insights | WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 101–109
- Language:
- URL:
- https://aclanthology.org/2024.insights-1.13
- DOI:
- Cite (ACL):
- Supriti Vijay and Daniel Hershcovich. 2024. Can Abstract Meaning Representation Facilitate Fair Legal Judgement Predictions?. In Proceedings of the Fifth Workshop on Insights from Negative Results in NLP, pages 101–109, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- Can Abstract Meaning Representation Facilitate Fair Legal Judgement Predictions? (Vijay & Hershcovich, insights-WS 2024)
- PDF:
- https://preview.aclanthology.org/jeptaln-2024-ingestion/2024.insights-1.13.pdf