Negrophobia and Reasonable Racism. Jody David Armour

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      Chapter Two

      THE “INTELLIGENT BAYESIAN”: RECKONING WITH RATIONAL DISCRIMINATION

      There is nothing more painful to me at this stage in my life than to walk down the street and hear footsteps and start thinking about robbery—then look around and see somebody White and feel relieved.

      —The Reverend Jesse Jackson, in a speech to a Black

      congregation in Chicago decrying Black-on-Black crime

      White America craves absolution. At least according to U.S. News & World Report it does. By admitting he sometimes fears young Black men, the Reverend Jackson “seemed to be offering sympathetic Whites something for which they hungered: absolution,” declared U.S. News.¹ For other journalists, Jackson’s comments were as much about vindication as absolution—in their view, his comments put an acceptable face on their own discriminatory beliefs and practices. Richard Cohen of the Washington Post, for example, announced in his column that Jackson’s remarks “pithily paraphrase what I wrote” in 1986.² He was referring to a 1986 column in which he asserted that if he were a shopkeeper, he would lock his doors “to keep young Black men out.” For Cohen, Jackson’s remarks proved that “it is not racism to recognize a potential threat posed by someone with certain characteristics.”

      Cohen’s advocacy of discrimination against young Black men raises a second argument advanced to justify acting on race-based assumptions, namely, that, given statistics demonstrating Blacks’ disproportionate engagement in crime, it is reasonable to perceive a greater threat from someone Black than someone White. Walter Williams, a conservative Black economist, refers to someone like Cohen as an “Intelligent Bayesian,” named for Sir Thomas Bayes, the father of statistics.³ For Williams, stereotypes are merely statistical generalizations, probabilistic rules of thumb that, when accurate, help people make speedy and often difficult decisions in a world of imperfect information. Whether “intelligent” is an apt adjective for a person who discriminates on the basis of stereotypes remains to be seen. For now we shall simply refer to such a person as a “Bayesian.”

      On its surface, the contention of the Bayesian appears relatively free of the troubling implications of the Reasonable Racist’s defense. While the Reasonable Racist explicitly admits his prejudice and bases his claim for exoneration on the prevalence of irrational racial bias, the Bayesian invokes the “objectivity” of numbers. The Bayesian’s argument is simple: “As much as I regret it, I must act differently toward Blacks because it is logical to do so.” The Bayesian relies on numbers that reflect not the prevalence of racist attitudes among Whites, but the statistical disproportionality with which Blacks commit crimes.

      As with any school of thought, Bayesians range from the vulgar to the more refined. An example of a vulgar Bayesian is Michael Levin, a social philosopher, who uses statistics to argue that a person jogging alone after dark is morally justified in fearing a young Black male ahead of him on a jogging track:

      It is widely agreed that young Black males are significantly more likely to commit crimes against persons than are members of any other racially identifiable group. Approximately one Black male in four is incarcerated at some time for the commission of a felony, while the incarceration rate for White males is between 2 and 3.5%.

      … Suppose, jogging alone after dark, you see a young Black male ahead of you on the running track, not attired in a jogging outfit and displaying no other information-bearing trait. Based on the statistics cited earlier, you must set the likelihood of his being a felon at 25…. On the other hand it would be rational to trust a White male under identical circumstances, since the probability of his being a felon is less than .05. Since whatever factors affect the probability of the Black attacking you—the isolation, your vulnerability—presumably affect the probability of a White attacking you as well, it remains more rational to be more fearful of the Black than of the White.⁴

      Levin erroneously suggests that because one out of four Black men is incarcerated for commission of a felony, the statistical benchmark a person should use in judging the risk of violent assault posed by a randomly selected young Black man is 25 percent. Levin’s statistics, however, say only that one in four Black males is incarcerated for a felony, not that one in four is incarcerated for a violent felony. Only the proportion of Blacks incarcerated for violent felonies can provide any kind of benchmark for judging relative risks of violent assault by race. But the typical African American male in the criminal justice system is not a violent offender.⁵ Most of the increase in the number of Blacks in the criminal justice system is attributable to the “War on Drugs” and stepped-up crackdowns on drug crimes.⁶ In fact, the majority of arrestees for violent offenses are White.⁷

      Assuming the woman who shot the suspected robber is a more refined Bayesian, she might frame her argument as follows. Although Blacks only make up 12 percent of the population, they are arrested for 62 percent of armed robberies.⁸ Therefore, the rate of robbery arrests among Blacks is approximately twelve times the rate among non-Blacks. In other words, if a defender had to make a purely race-based assessment of the risk of armed robbery, it would be approximately twelve times more probable that any given Black person is a robber than a non-Black.⁹ Even assuming considerable bias in police arrests, the refined Bayesian might conclude, no one can honestly say that actual rates of robbery by race are even close.

      One can concede the Bayesian’s point that the rates of robbery by race are “not close” and still ask, “So what?” It is far from clear what sorts of group-based robbery rates would justify the judgment that any given member of the group presents a sufficiently high risk of robbery to be deemed “suspicious.” To make the point a different way, imagine I have two drawers, one white and the other black. Into the white drawer I pour one thousand marbles, 999 of which are green and one of which is red. Into the black drawer I also pour one thousand marbles, but this time I included twelve times the number of red ones. Thus the black drawer contains twelve red and 988 green marbles, or slightly over 1 percent red marbles. Twelve times a very small fraction may still be a very small fraction.

      Now, substitute the social groups “Whites” and “Blacks” for the white and black drawers respectively, make the red marbles the members of each group arrested for violent crimes, and the problem with reading too much into the relative rates of robbery by race becomes evident. Blacks arrested for violent crimes comprised less than 1 percent of the Black population in 1994, and only 1.86 percent of the Black male population.¹⁰ Recall that even a vulgar Bayesian like Levin—who equates being incarcerated with being incarcerated for a violent crime—asserts that because the incarceration rate for White males is between 2 and 3.5 percent, “it would be rational to trust a White male” you see ahead of you while jogging alone after dark. By this Bayesian’s own logic, therefore, since Blacks arrested for violent crimes make up less than 1.9 percent of the Black male population, “it would be rational to trust a [Black] male” you ran into in the dark.

      Let’s assume—perhaps erroneously—that the rates of robbery by race are in some marginal sense “statistically significant.” Thus, the Bayesian asserts that he would never employ race as the sole or even dominant risk factor in assessing someone’s dangerousness. “I merely seek to give race its correct incremental value in my calculations,” he assures us with all the aplomb of Mr. Spock. Thus, in addition to race, he carefully weighs other personal characteristics—such as youth, gender, dress, posture, body movement, and apparent educational level—before deciding how to respond. Having tallied up these “objective” indices of criminality, the Intelligent Bayesian argues that his conduct was reasonable (and thus not morally blameworthy) because it was “rational.”

      A threshold problem with the Bayesian’s profession of pristine rationality concerns the “scrambled СКАЧАТЬ