  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.
