Abstract

Big data and artificial intelligence have opened up unprecedented perspectives for our ability to predict future states of the world. The same is true in the legal field, where scholars and practitioners are growingly drawn to the forecasting capabilities of algorithms. In recent years, prediction models have not only fed a lively theoretical debate on legal computability and predictive justice. Still, they have also inspired a number of applications spanning from intelligent platforms for workforce management to innovative tools for the judicial assessment of recidivism risk. In such a scenario, we need a reflection on the impact that computational heuristics can have on the very complexion of law. Seen up close, the use of predictive analytics techniques in legal settings is often affected by issues that range from inherent epistemic fragilities to the risk of turning into rights violations. This paper provides a critical account of computational prediction and its hidden pitfalls. Our first goal is to lay the groundwork for an in-depth analysis of the theoretical and practical implications that predictive heuristics may have for law. The second one is to present augmented intelligence – the cooperative integration between humans and machines – as a reference paradigm to mitigate the risks of prediction and, more in general, to inspire the computational evolution of legal science and practice.