Data-Centric Machine Learning in the Legal Domain

• URL: http://arxiv.org/abs/2201.06653v1
• Date: Mon, 17 Jan 2022 23:05:14 GMT
• Title: Data-Centric Machine Learning in the Legal Domain
• Authors: Hannes Westermann, Jaromir Savelka, Vern R. Walker, Kevin D. Ashley, Karim Benyekhlef
• Abstract summary: This paper explores how changes in a data set influence the measured performance of a model. Using three publicly available data sets from the legal domain, we investigate how changes to their size, the train/test splits, and the human labelling accuracy impact the performance. The observed effects are surprisingly pronounced, especially when the per-class performance is considered.
• Score: 0.2624902795082451
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Machine learning research typically starts with a fixed data set created early in the process. The focus of the experiments is finding a model and training procedure that result in the best possible performance in terms of some selected evaluation metric. This paper explores how changes in a data set influence the measured performance of a model. Using three publicly available data sets from the legal domain, we investigate how changes to their size, the train/test splits, and the human labelling accuracy impact the performance of a trained deep learning classifier. We assess the overall performance (weighted average) as well as the per-class performance. The observed effects are surprisingly pronounced, especially when the per-class performance is considered. We investigate how "semantic homogeneity" of a class, i.e., the proximity of sentences in a semantic embedding space, influences the difficulty of its classification. The presented results have far reaching implications for efforts related to data collection and curation in the field of AI & Law. The results also indicate that enhancements to a data set could be considered, alongside the advancement of the ML models, as an additional path for increasing classification performance on various tasks in AI & Law. Finally, we discuss the need for an established methodology to assess the potential effects of data set properties.

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