Here’s a scary development from our big-data reality: predictive sentencing for defendants based on statistics which suggest future-crime risk. The actual offense committed is only part of the equation, with much thornier things, like race and class, considered. It’s often referred to as “smart sentencing,” but you might not agree if you happen to fit into the wrong statistical quadrant. It’s math run amok. From Sonja B. Starr at the New York Times:

“ANN ARBOR, Mich. — IN a recent letter to the United States Sentencing Commission, Attorney General Eric H. Holder Jr. sharply criticized the growing trend of evidence-based sentencing, in which courts use data-driven predictions of defendants’ future crime risk to shape sentences. Mr. Holder is swimming against a powerful current. At least 20 states have implemented this practice, including some that require risk scores to be considered in every sentencing decision. Many more are considering it, as is Congress, in pending sentencing-reform bills.

Risk-assessment advocates say it’s a no-brainer: Who could oppose ‘smarter’ sentencing? But Mr. Holder is right to pick this fight. As currently used, the practice is deeply unfair, and almost certainly unconstitutional. It contravenes the principle that punishment should depend on what a defendant did, not on who he is or how much money he has.

The basic problem is that the risk scores are not based on the defendant’s crime. They are primarily or wholly based on prior characteristics: criminal history (a legitimate criterion), but also factors unrelated to conduct. Specifics vary across states, but common factors include unemployment, marital status, age, education, finances, neighborhood, and family background, including family members’ criminal history.”