Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy Read Online Free PDF

powered by haphazard data gathering and spurious correlations, reinforced by institutional inequities, and polluted by confirmation bias. In this way, oddly enough, racism operates like many of the WMDs I’ll be describing in this book.

    In 1997, a convicted murderer, an African American man named Duane Buck, stood before a jury in Harris County, Texas. Buck had killed two people, and the jury had to decide whether he would be sentenced to death or to life in prison with the chance of parole. The prosecutor pushed for the death penalty, arguing that if Buck were let free he might kill again.
    Buck’s defense attorney brought forth an expert witness, a psychologist named Walter Quijano, who didn’t help his client’s case one bit. Quijano, who had studied recidivism rates in the Texas prison system,made a reference to Buck’s race, and during cross-examination the prosecutor jumped on it.
    “You have determined that the…the race factor, black, increases the future dangerousness for various complicated reasons. Is that correct?” the prosecutor asked.
    “Yes,” Quijano answered. The prosecutor stressed that testimony in her summation, and the jury sentenced Buck to death.
    Three years later, Texas attorney general John Cornyn foundthat the psychologist had given similar race-based testimony in six other capital cases, most of them while he worked for the prosecution. Cornyn, who would be elected in 2002 to the US Senate, ordered new race-blind hearings for the seven inmates. In a press release, he declared: “It is inappropriate to allow race to be considered as a factor in our criminal justice system….The people of Texas want and deserve a system that affords the same fairness to everyone.”
    Six of the prisoners got new hearings but were again sentenced to death. Quijano’s prejudicial testimony, the court ruled, had not been decisive.Buck never got a new hearing, perhaps because it was his own witness who had brought up race. He is still on death row.
    Regardless of whether the issue of race comes up explicitly at trial, it has long been a major factor in sentencing. A University of Maryland study showed that in Harris County, which includes Houston,prosecutors were three times more likely to seek the death penalty for African Americans, and four times more likely for Hispanics, than for whites convicted of the same charges. That pattern isn’t unique to Texas. According to the American Civil Liberties Union,sentences imposed on black men in the federal system are nearly 20 percent longer than those for whites convicted of similar crimes. And though they make up only 13 percent of the population,blacks fill up 40 percent of America’s prison cells.
    So you might think that computerized risk models fed by data would reduce the role of prejudice in sentencing and contribute to more even-handed treatment. With that hope,courts in twenty-four states have turned to so-called recidivism models. These help judges assess the danger posed by each convict. And by many measures they’re an improvement. They keep sentences more consistent and less likely to be swayed by the moods and biases of judges. They also save money by nudging down the length of the average sentence. (It costs anaverage of $31,000 a year to house an inmate, and double that in expensive states like Connecticut and New York.)
    The question, however, is whether we’ve eliminated human bias or simply camouflaged it with technology. The new recidivism models are complicated and mathematical. But embedded within these models are a host of assumptions, some of them prejudicial. And while Walter Quijano’s words were transcribed for the record, which could later be read and challenged in court, the workings of a recidivism model are tucked away in algorithms, intelligible only to a tiny elite.
    One of the more popular models, known as LSI–R, or Level of Service Inventory–Revised, includes a lengthy questionnaire for the prisoner to fill out. One