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Judges Eviscerate Statistical Defense to Crime
Rezul News/10710754
Why Black Drivers Are Disproportionately Pulled Over By Police
WASHINGTON - Rezul -- A federal appeals court has just unanimously eviscerated (https://www.google.com/url?esrc=s&q=&rc...) a defense based upon statistical evidence supposedly showing that police disproportionately stop Black drivers.
Such a showing is irrelevant even if true, but more importantly blasting the evidence of racial discrimination as statistically flawed.
This seems to be an additional example of the old adage that "Figures Don't Lie But Lawyers Don't Figure," says mathematician and law professor John Banzhaf, who coined the new adage in his:
One Man, ? Votes: Mathematical Analysis of Voting Power and Effective Representation
More on Rezul News
applying his mathematical game theory "Banzhaf Index" to analyze complex electoral systems. See also:
Law Professor's Mathematical Tool Helps Identify The Most Dangerous Terrorists (https://www.sharewise.com/de/news_articles/Law_Professors_Mathematical_Tool_Helps_Identify_The_Most_Dangerous_Terrorists_ValueWalk_20230504_2357)
Here e a felon, charged with possessing a firearm after he fled from the police during a traffic stop after which the gun was found in his vehicle, convinced a lower court judge, despite clear and uncontradicted evidence that he did in fact posses a gun, to dismiss his indictment on the ground that it was the product of selective law enforcement against Black drivers.
But, as noted by the court in quoting from an expert witness:
■ "examining the raw percentages of drivers stopped by race is meaningless unless, at a minimum, those percentages can be compared against an estimate of the population of drivers in the jurisdiction of interest"
■ "the population of persons who live in an area often serves as a poor representation of persons who drive in an area or who are at risk for being stopped by the police"
More on Rezul News
■ "[p]olice typically deploy more officers to neighborhoods with higher crime rates" and that such "police deployment variables often correlate with race and ethnicity in America's urban neighborhoods" [emphasis added]
The appellate court aid that, even if the flawed statistical evidence did prove that Black drivers were stopped disproportionately, the defendant's argument that he should not be prosecuted "would mean that any Black driver stopped by [a Richmond Police Department] officer could prove discriminatory purpose based on the exact same statistical and historical evidence, regardless of the actual motives of the officer who stopped his car. The consequence would be to jeopardize the ability of Richmond Police Department officers to enforce traffic laws against Black drivers in Richmond."
The three judges went on to rule that "if one were to show, hypothetically, that 65% of the drivers stopped in Jonesville were Black when 65% of the drivers in Jonesville were Black, the 65%-of-drivers-stopped statistic establishes nothing, because there is no disparity. We have repeatedly noted that 'raw data' lacking 'an appropriate basis for comparison' cannot satisfy any element of an Equal Protection claim."
http://banzhaf.net/ jbanzhaf3ATgmail.com @profbanzhaf
Such a showing is irrelevant even if true, but more importantly blasting the evidence of racial discrimination as statistically flawed.
This seems to be an additional example of the old adage that "Figures Don't Lie But Lawyers Don't Figure," says mathematician and law professor John Banzhaf, who coined the new adage in his:
One Man, ? Votes: Mathematical Analysis of Voting Power and Effective Representation
More on Rezul News
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applying his mathematical game theory "Banzhaf Index" to analyze complex electoral systems. See also:
Law Professor's Mathematical Tool Helps Identify The Most Dangerous Terrorists (https://www.sharewise.com/de/news_articles/Law_Professors_Mathematical_Tool_Helps_Identify_The_Most_Dangerous_Terrorists_ValueWalk_20230504_2357)
Here e a felon, charged with possessing a firearm after he fled from the police during a traffic stop after which the gun was found in his vehicle, convinced a lower court judge, despite clear and uncontradicted evidence that he did in fact posses a gun, to dismiss his indictment on the ground that it was the product of selective law enforcement against Black drivers.
But, as noted by the court in quoting from an expert witness:
■ "examining the raw percentages of drivers stopped by race is meaningless unless, at a minimum, those percentages can be compared against an estimate of the population of drivers in the jurisdiction of interest"
■ "the population of persons who live in an area often serves as a poor representation of persons who drive in an area or who are at risk for being stopped by the police"
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■ "[p]olice typically deploy more officers to neighborhoods with higher crime rates" and that such "police deployment variables often correlate with race and ethnicity in America's urban neighborhoods" [emphasis added]
The appellate court aid that, even if the flawed statistical evidence did prove that Black drivers were stopped disproportionately, the defendant's argument that he should not be prosecuted "would mean that any Black driver stopped by [a Richmond Police Department] officer could prove discriminatory purpose based on the exact same statistical and historical evidence, regardless of the actual motives of the officer who stopped his car. The consequence would be to jeopardize the ability of Richmond Police Department officers to enforce traffic laws against Black drivers in Richmond."
The three judges went on to rule that "if one were to show, hypothetically, that 65% of the drivers stopped in Jonesville were Black when 65% of the drivers in Jonesville were Black, the 65%-of-drivers-stopped statistic establishes nothing, because there is no disparity. We have repeatedly noted that 'raw data' lacking 'an appropriate basis for comparison' cannot satisfy any element of an Equal Protection claim."
http://banzhaf.net/ jbanzhaf3ATgmail.com @profbanzhaf
Source: Public Interest Law Professor John Banzhaf
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