Abstract: Despite strong scholarly interest in explainable features in AI (XAI), there is little experimental work to gauge the effect of XAI on human-AI cooperation in legal tasks. We study the effect of textual highlighting as an XAI feature used in tandem with a machine learning (ML) generated summary of a legal complaint. In a randomized controlled study we find that the XAI has no effect on the proportion of time participants devote to different sections of a legal document, but we identify potential signs of XAI's influence on the reading process. XAI attention-based highlighting may change the spatio-temporal distribution of attention allocation, a result not anticipated by previous studies. Future work on the effect of XAI in legal tasks should measure process as well as outcomes to better gauge the effects of XAI in legal applications.