Fresh back from trademarking the Sentence-O-Matic 1000, Jake DiMare proudly advised me that this idea wasn’t dead yet, as demonstrated by Judge Richard Kopf’s continued push toward sentencing empiricism. Oh, that Jake. Oh, that judge.
While yanking Attorney General Eric Holder’s chain for wanting his cake and eating it too, by decrying the use of race as a predictor of recidivism while prisons are filled to the brim with men of color put there on Holder’s watch for living the lifestyle of poor black rather than Attorney General (or president) elite black, the judge proffers an alternative list of factors as predictors. Notably, race is crossed off the list.
Let’s face it friends, “race” is low-hanging fruit. It is too easy to attack, although the social science data on race when used as a predictive metric for sentencing is not really about genetics (“race”) as a causative factor in crime. It is about being correlated with crime, and there is a huge difference between the two (causation and correlation). But the word “race” is too freighted with the notion of “discrimination,” so let’s just agree for the sake of argument that empirical data on “race” will never be used at sentencing.
And indeed, race is certainly a flash point, despite its utility in what passes as empiricism these days. So here’s the balance of the list, taken from James C. Oleson’s article on data-driven sentencing.
1. Criminal Companions
2. Criminogenic Needs
3. Antisocial Personality
4. Adult Criminal History
6. Pre-Adult Antisocial Behavior
7. Family Rearing Practices
8. Social Achievement
9. Interpersonal Conflict
10. Current Age
11. Substance Abuse
12. Family Structure
13. Intellectual Functioning
14. Family Criminality
16. Socio-Economic Status of Origin
17. Personal Distresss
These 16 factors create a construct to empirically predict who will go out and commit crimes. It takes little effort to deconstruct these factors in terms of race, poverty, educational opportunity, future prospects. These are all proxies (except gender) for race, even though race is crossed off.
To the extent that Judge Kopf calls Holder intellectually dishonest, there is no doubt that’s true. Judge Kopf concludes that Holder is an “ostrich,” hiding his head in the sand when calling for the elimination of race as a consideration, while using every manifestation of racial prejudice available to justify the same outcome.
I suspect that Holder is hardly an “ostrich,” but only because I don’t believe the attorney general isn’t well aware of what Judge Kopf is saying. The AG knows that the system is replete with proxies for racial disparities that impact blacks disproportionately and perpetuate those disparities. If daddy is in prison, son will be there soon enough. But if we don’t mention that daddy is black, then it’s racially neutral, right? Run the numbers, guys.
As I responded to Jake’s irrational exuberance over the billions he plans to make with the Sentence-O-Matic 1000, this is still just pigeonholing people. As was noted in the comments to the judge’s post, these are all factors that are subsumed in sentencing now under §3553(a), to the extent they apply. There is no limit to what is thrown against the wall to see if it sticks.
And it’s completely understandable from the perspective of a judge, the person whose job it is to fashion the “right” sentence, that there be concrete parameters so that it’s easier to stick each defendant into a pigeonhole. It makes for a far cleaner mess. Who doesn’t want to believe they’ve done the right thing because 16 factors say so? No more nagging, sleepless nights pondering lives ruined. The Sentence-O-Matic 1000 says you gave the right sentence, and so you did, dammit.
As an aside, the notion that data-driven sentences will somehow result in a reduced prison population eludes me. Using these factors to determine recidivism is neutral. They’re just factors. They no more suggest a greater or lesser number of people going to prison; they merely provide a comparative basis between defendants.
But will the comfort of knowing that there is some data-driven basis to justify a sentence change how long or short an individual sentence will be? Not unless somebody decides that life plus cancer isn’t the baseline sentence for anyone who commits a federal offense. But that’s a different problem, and I digress.
Where in that list of predictive factors is there anything about a teenager who has to fight his way down the street to survive, because he lives in a tough neighborhood where nice boys end up dead? Where in that list is there a chance for the child of a drug-addled parent to go to college and become a nuclear physicist? Where in that list does a kid with a pot bust go on to become president?
The data says these things won’t happen, and they most assuredly won’t as long as we’re playing the odds. These are the same odds that inform a judge that the cop on the stand is more likely right than lying, and the defendant in the dock is more likely guilty than railroaded. Because of these odds, lying cops get a pass, and innocent defendants get convicted. And kids who grew up under unpleasant circumstances not of their making are more likely to be recidivists.
Yes, yes, yes. The odds, the data-driven odds, are all probably true. I concede the point. But they are not absolute. They are not immutable. The rare person can break free of the odds and go on to create great music, beautiful art, a cure for cancer. But when concern over facilitating a pseudo-scientific approach to sentencing becomes more valuable than taking a hard individual look at every human being standing before a judge for sentence, we are guaranteed that they will never climb out of the pigeonhole into which they’ve been dumped.
If we allow ourselves to fall into the facile methodology of locking in our worst factors because the data tells us that these will keep the recidivists off the street, we condemn the best along with the worst. We freeze the status quo, and yawn as generations repeat the failings of their predecessors because the data refuses to give them the chance to be better.
There is something warming, comforting, about removing the burden, the responsibility, for destroying people’s lives by using empiricism. It’s no longer the judge’s fault for depriving a human being of the chance to break free of bad parents, bad neighborhoods, bad schools and, yes, racial prejudice. The data made him do it.
And I still fail to see how this means we end up with fewer people in prison. It just means we feel better about putting them there.
Update: Judge Kopf posts an email from Dr. Oleson, which includes this:
People dislike the “sentence-o-matic 1000” but rejecting algorithms and automation does not prevent assessments of risk – it just means that the human estimates are likely to be more idiosyncratic and less accurate
Dr. Oleson is clearly correct that they are likely to be more idiosyncratic. As to less accurate, that depends on whether the rejection of algorithms and automation means we remain constrained by the pigeonholes or expand the factors to everything, data-be-damned, that reflects the full panoply of human life, character, nature and circumstances, no matter how varied they are in any case.
Mind you, for those lawyers who fall back on the routine sentencing arguments rather than provide the court with insight into your client’s entire life, world and circumstances, this won’t matter. You’ve pigeonholed your client already. For those judges who only care about the shallow, pedestrian factors, same thing.
For those who remember that a human life is at risk, and strive to do better, to provide a full and rich picture of the person before a court, those idiosyncratic estimates are what distinguishes humanity from machines. As for accuracy, who are we kidding?