Prediction, persuasion, and the jurisprudence of behaviourism

There is a growing literature critiquing the unreflective application of big data, predictive analytics, artificial intelligence, and machine-learning techniques to social problems. Such methods may reflect biases rather than reasoned decision making. They also may leave those affected by automated sorting and categorizing unable to understand the basis of the decisions affecting them. Despite these problems, machine-learning experts are feeding judicial opinions to algorithms to predict how future cases will be decided. We call the use of such predictive analytics in judicial contexts a jurisprudence of behaviourism as it rests on a fundamentally Skinnerian model of cognition as a black-boxed transformation of inputs into outputs. In this model, persuasion is passé; what matters is prediction. After describing and critiquing a recent study that has advanced this jurisprudence of behaviourism, we question the value of such research. Widespread deployment of prediction models not based on the meaning of important precedents and facts may endanger the core rule-of-law values.