Masha Medvedeva

Masha Medvedeva, Martijn Wieling, Michel Vols

In this paper we discuss the implications of using machine learning for judicial decision-making in situations where human rights may be infringed. We argue that the use of such tools in these situations should be limited due to inherent status quo bias and dangers of reverse-engineering. We discuss that these issues already exist in the judicial systems without using machine learning tools, but how introducing them might exacerbate them.

Angelo Basile, Gareth Dwyer, Maria Medvedeva et al.

We describe our participation in the PAN 2017 shared task on Author Profiling, identifying authors' gender and language variety for English, Spanish, Arabic and Portuguese. We describe both the final, submitted system, and a series of negative results. Our aim was to create a single model for both gender and language, and for all language varieties. Our best-performing system (on cross-validated results) is a linear support vector machine (SVM) with word unigrams and character 3- to 5-grams as features. A set of additional features, including POS tags, additional datasets, geographic entities, and Twitter handles, hurt, rather than improve, performance. Results from cross-validation indicated high performance overall and results on the test set confirmed them, at 0.86 averaged accuracy, with performance on sub-tasks ranging from 0.68 to 0.98.