by Hans Bader
A judge recently found that the City of Richmond racially profiles black motorists, dismissing the indictment of a black convicted felon accused of illegally possessing a gun. The judge did not find that defendant Keith Moore had been treated differently than a similarly situated white motorist. Instead, he ruled that Richmond police stops are racially discriminatory, based on statistics showing blacks are stopped and arrested at much higher rates than whites; and based on Richmond’s past “history of discrimination,” such as racialized zoning and redlining, and the “Confederate foundations” of the Richmond Police Department. “The Court will not require Moore to provide evidence of similarly situated individuals to prove his selective enforcement claim,” wrote the judge.
This is likely to create big problems for the City of Richmond, potentially leading to many criminals being released from jail. If a judge claims racial discrimination happened, he should identify what policies are racially discriminatory, or give concrete examples of discrimination, so that the problem can be fixed. But Judge Gibney failed to do that in his February 12 ruling in United States v. Keith Rodney Moore. So now the City is deemed guilty of discrimination, based on things no individual police officer can change (such as city-wide statistics), and things that literally no one can change (such as the confederate origins of the police department and Richmond’s segregated past). If other judges follow this flawed ruling, other criminals can also have their indictments dismissed based on city-wide statistics, even if it is undisputed that they committed the crime for which they were arrested.
Although the judge cited statistical disparities, he did not cite any specific police practices that led to blacks being stopped at higher rates, as he should have done if police were actually at fault. In Smith v. City of Jackson (2005), the Supreme Court ruled that even unintentional discrimination (disparate-impact) cannot be proved through statistics unless “specific” practices are identified that caused the “statistical disparities.” The disparities themselves are not enough. Continue reading