Predictive Policing and Technological Due Process
Police departments have been increasingly crunching data to identify criminal hot spots and to allocate policing resources to address them. Predictive policing has been around for a while without raising too many alarms. Given the daily proof that we live in a surveillance state, such policing seems downright quaint. Putting more police on the beat to address likely crime is smart. In such cases, software is not making predictive adjudications about particular individuals. Might someday governmental systems assign us risk ratings, predicting whether we are likely to commit crime? We certainly live in a scoring society. The private sector is madly scoring us. Individuals are denied the ability to open up bank accounts; they are identified as strong potential hires (or not); they are deemed “waste” not worthy of special advertising deals; and so on. Private actors don’t owe us any process, at least as far as the Constitution is concerned. On the other hand, if governmental systems make decisions about our property (perhaps licenses denied due to a poor scoring risk), liberty (watch list designations leading to liberty intrusions), and life (who knows with drones in the picture), due process concerns would be implicated.
What about systems aimed at predicting high-crime locations, not particular people? Do those systems raise the sorts of concerns I’ve discussed as Technological Due Process? A recent NPR story asked whether algorithmic predictions about high-risk locations can form the basis of a stop and frisk. If someone is in a hot zone, can that very fact amount to reasonable suspicion to stop someone in that zone? During the NPR segment, law professor Andrew Guthrie Ferguson talked about the possibility that the computer’s prediction about the location may inform an officer’s thinking. An officer might credit the computer’s prediction and view everyone in a particular zone a different way. Concerns about automation bias are real. Humans defer to systems: surely a computer’s judgment is more trustworthy given its neutrality and expertise? Fallible human beings, however, build the algorithms, investing them with bias, and the systems may be filled with incomplete and erroneous information. Given the reality of automated bias, police departments would be wise to train officers about automation bias, which has proven effective in other contexts. In the longer term, making pre-commitments to training would help avoid unconstitutional stops and wasted resources. The constitutional question of the reasonableness of the stop and frisk would of course be addressed on a retail level, but it would be worth providing wholesale protections to avoid wasting police time on unwarranted stops and arrests.
H/T: Thanks to guest blogger Ryan Calo for drawing my attention to the NPR story.