Recent studies have suggested that weight pruning, e.g. using lottery ticket extraction techniques (Frankle and Carbin, 2018), comes at the risk of compromising the group fairness of machine learning models (Paganini, 2020; Hooker et al., 2020), but to the best of our knowledge, no one has empirically evaluated this hypothesis at scale in the context of natural language processing. We present experiments with two text classification datasets annotated with demographic information: the Trustpilot Corpus (sentiment) and CivilComments (toxicity). We evaluate the fairness of lottery ticket extraction through layer-wise and global weight pruning across three languages and two tasks. Our results suggest that there is a small increase in group disparity, which is most pronounced at high pruning rates and correlates with instability. The fairness of models trained with distributionally robust optimization objectives is sometimes less sensitive to pruning, but results are not consistent. The code for our experiments is available at https://github.com/vpetren/fairness_lottery.