Abstract

The criminal justice (CJ) system has embraced the use of algorithmic tools. They are employed as decision aids, from policing to parole decisions. This may bring benefits such as improved efficiency and consistency, but also raises many concerns. We will discuss the gap between the promised benefits and what is happening in practice, highlighting challenges around data, ethics, and regulations.

On the data front, a key limitation of current ML for CJ research is overfocus on only a few datasets like COMPAS . Moreover, domain context is rarely taken into account even for these datasets. We will discuss our work on enabling researchers to 1) better utilise existing CJ datasets, and 2) create new datasets using human-machine cooperation. The latter relates to our effort to increase transparency in the UK court system by making trial transcripts amenable to quantitative research.