  Every legal case sets a precedent by developing the law in one of the
following two ways. It either expands its scope, in which case it sets positive
precedent, or it narrows it, in which case it sets negative precedent. Legal
outcome prediction, the prediction of positive outcome, is an increasingly
popular task in AI. In contrast, we turn our focus to negative outcomes here,
and introduce a new task of negative outcome prediction. We discover an
asymmetry in existing models' ability to predict positive and negative
outcomes. Where the state-of-the-art outcome prediction model we used predicts
positive outcomes at 75.06 F1, it predicts negative outcomes at only 10.09 F1,
worse than a random baseline. To address this performance gap, we develop two
new models inspired by the dynamics of a court process. Our first model
significantly improves positive outcome prediction score to 77.15 F1 and our
second model more than doubles the negative outcome prediction performance to
24.01 F1. Despite this improvement, shifting focus to negative outcomes reveals
that there is still much room for improvement for outcome prediction models.
