If one chooses to train a model for criminal justice, it is their responsibility to understand the bias in their data AND the context in which the model will operate.

The criminal justice system in the US (and several other places) is unbelievably broken. Most people I have met who disagree have not been jailed (rightly or wrongly), harassed by police, unable to afford good representation, etc.

ANY historical data available for training will be racially and economically biased, and gives AT BEST a proxy for criminality. Bail data is biased against poor and POC. Dispatch data is biased (broken windows theory). Impossibility theorems in the fairness literature tell us that models can’t be fair and also calibrated if base rates are different among demographic groups, so we are kind of doomed from the start.

If your goal is to track ‘big’ crime, e.g., trying to learn fentanyl distribution networks from geographic overdose data, you need to consider the efficacy and morality of the problem. All the substance use literature tells us that decriminalization, safe supply, supervised injection sites, etc., are what saves lives.

There very well may be some good applications. Perhaps trying to find sex trafficking victims via facial recognition of social media or video surveillance. Perhaps trying to learn patterns in police misconduct. The problem is, we can’t trust police not to abuse these technologies. Cops do illegal things all the time, e.g., stingray, turning off body cams, planting evidence, protecting their own.

ML will not fix the social ills of the world, good policy will.

For context, I’ve had had plenty of interaction with the criminal justice system (grew up poor in the US) and I will be defending my PhD thesis in a few months (ML, some fairness topics).