Abstract
One of the main barriers to effective prediction systems in the legal domain is the very limited availability of relevant data. This paper discusses the particular case of the Federal Court of Canada, and describes some perspectives on how best to overcome these problems. Part of the process involves an automatic annotation system, supervised by a manual annotation process. Several state-of-the-art methods on related tasks are presented, as well as promising approaches leveraging recent advances in natural language processing, such as vector word representations or recurrent neural networks. The insights outlined in the paper will be further explored in a near future, as this work is still an ongoing research.
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Notes
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United States Supreme Court decisions dataset http://scdb.wustl.edu/.
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SOQUIJ website: http://soquij.qc.ca/.
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Acknowledgments
We thank José Bonneau for his description of the difficulties in accessing legal court decisions. We also thank Diego Maupomé and Antoine Briand for their valuable comments.
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Queudot, M., Meurs, MJ. (2018). Artificial Intelligence and Predictive Justice: Limitations and Perspectives. In: Mouhoub, M., Sadaoui, S., Ait Mohamed, O., Ali, M. (eds) Recent Trends and Future Technology in Applied Intelligence. IEA/AIE 2018. Lecture Notes in Computer Science(), vol 10868. Springer, Cham. https://doi.org/10.1007/978-3-319-92058-0_85
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