Word is police are buying statistical algorithms which purport to predict crime. Not just at the neighborhood- or block-level, but for persons—meaning for you. Which of our readers is most likely to snatch up a knife from the drawer and run amok? Some enterprising entrepreneur is willing to charge us to answer.
That’s not the new news. The twist is that there are now algorithms to predict which cops will turn rogue. According to Five Thirty Eight’s “We Now Have Algorithms To Predict Police Misconduct“:
Many police departments have early warning systems — software that tracks each officer’s performance and aims to forecast potential problems. The systems identify officers with troubling patterns of behavior, allowing superiors to monitor these cops more closely or intervene and send them to counseling.
What goes into these computer programs?
Incidents that officers deemed stressful were a major contributor; cops who had taken part in suicide and domestic-violence calls earlier in their shifts were much more likely to be involved in adverse interactions later in the day.
Also: previous incidents of bad behavior on the part of individual cops. These are all, of course, just the sorts of things that feed into the “algorithm” that police supervisors have always used to sniff out bad apples. That informal decision making process was not perfect, but, as everyday experience confirms, it must have at least been adequate, if not superior to good enough. So why muck things up with an official piece of software?
I’ll give you one good reason: bureaucratic cowardice. Scenario 1: “Don’t blame the department, Mr Mayor, the Rogue-o-Matic 3000 only gave Officer Smith a 12.391771% of going on the take.” Scenario 2: “I’m sorry, sergeant, we just can’t promote you. Men with PhDs in computer science have developed a sophisticated and powerful program that says you’re likely to beat suspects with your truncheon.”
Bureaucrats love avoiding responsibility; they also adore taking credit. Using some sort of official screening algorithm lets them do both. Avoiding responsibility we’ve just seen. Taking credit is easy, too. “Commissioner, here’s my bi-semi-quarterly amended report, which my office spent the last eight weeks writing, which proves that since we’ve adopted the Screenerator XM-550-FP, we prevented 172.3 crimes, one of which was a rape!, which is as sexist a crime as there is.” That these nonexistent crimes never existed everybody forgets.
Blanket screening does more harm than good. The article tells us when one algorithm was employed to ID bad apples “50 percent of the flagged officers in the data set did not” go rogue. But don’t worry. This is a New & Improved! algorithm which “flags 15 percent fewer officers than the old one.”
False positives—saying good cops will be bad ones—are an enormous problem. Huge number of reputations tainted or ruined, false suspicions raised. False negatives—saying bad cops are good—are even worse. Some cop who otherwise would have been obviously flagged by some supervisor is given a pass because a computer says he’s okay. Or people who would not have been otherwise flagged are never looked at.
The main problem with using a computer-machine-deep-learning-neural-network-big-data-algorithm is that the results appear better than they are. Worse, they appear “objective.” People trust them too much. Why?
Well, haven’t computers beat Grand Masters in chess and go? Then they surely can predict which cop will go rogue! Right?
No. To paraphrase Young Frankenstein “Chess and go are Tinkertoys! We’re talking about the central nervous system!” and about the entirety of human lives, facets which outstrip the complexity of these trivial games to the same degree an interplanetary spaceship does compared to a thrown rock. What do I mean?
All these algorithms, including any future ones that might be invented, work in the same way. A list of measurements thought probative of the outcome (a cop going rogue), are compiled. There is no rule on what this list of measurements should be: they can be anything. For cops going rogue these might be: the number of previous incidents of misbehavior, age, job function, number of donuts eaten and whether jelly is preferred over plain, etc., etc.
Don’t scoff at the donuts. It could be that cops who eat more jelly than plain donuts go rogue more often. Who knows?
In the end, the measurements are fed into the algorithm and they mathematically interact with each and with the outcome. The problem is: nobody knows which measurements are best and, worse, nobody knows how the measurements taken interact with one another. The complexity is too great. The accuracy rate is anemic.
We’ll next week do a part II on the ethics of precrime. So let’s here only discuss the nature of the algorithms themselves.