Читать книгу Negrophobia and Reasonable Racism - Jody David Armour - Страница 17

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 eggs” problem described earlier—that is, the practical impossibility of unscrambling the rational and irrational sources of racial fears. For countless Americans, fears of Black violence stem from, among other things, the complex interaction of cultural stereotypes, racial antagonisms, and unremitting overrepresentations of Black violence in the mass media. As for the mass media, especially television news, recall the letter in the Introduction from the would-be Bayesian who remarked, “If I saw Blacks in my neighborhood I would be on the lookout, and for a good reason.” The “good reason” he cites for his hypervigilance about Blacks is television. Few Americans keep copies of FBI Uniform Crime Reports by their bedsides: when asked in a Los Angeles Times survey (February 13, 1994) from where they got their information about crime, 65 percent of respondents said they learned about it from the mass media. But television journalism on crime and violence has been proven to reveal, and project, a consistent racial bias.¹¹

Even if media reporting on crime and violence were not biased, our minds simply do not process information about Blacks and other stereotyped groups the way the Bayesian assumes. The Bayesian assumes that our minds can passively mirror the world around us, that they can operate like calculators, and that social stereotypes can be represented in our minds as mere bits of statistical information, as malleable and subject to ongoing revision as the batting averages of active majorleague baseball players. Each of these assumptions flies in the face of what modern psychology reveals about the workings of the human mind.

As is described in detail in chapter 6, social stereotypes are not mere bits of statistical information but rather well-learned sets of associations among groups and traits established in children’s memories before they reach the age of judgment. And once a stereotype becomes entrenched in our memory, it takes on a life of its own. Case studies have demonstrated that once an individual internalizes a cultural stereotype, she unconsciously interprets experiences to be consistent with the underlying stereotype, selectively assimilating facts that validate the stereotype while disregarding those that do not.¹² The tendency of individuals to reject or ignore evidence that conflicts with their cultural stereotypes expresses itself in many forms, perhaps none as perplexing as the backhanded “compliment” some White liberals think appropriate to bestow on “deserving” Blacks: “I don’t think of you as Black.” For Blacks who harbor the hope that their personal achievements can “uplift the race” by upending stereotypes, these clumsy bouquets are deeply disturbing. The more success you achieve, the less likely that your success will redound to the reputational benefit of your community. In the words of Evelyn Lewis, the first Black woman to make partner in a major San Francisco law firm, “[W]hat you do well will reflect well on you, but only as an individual. And what you do poorly—well, that’s when what you do will be dumped on the whole race.”¹³ To the extent that the Bayesian aggressively assimilates negative statistical information about Blacks while remaining oblivious to contradictory or positive statistical information, she undermines her claim of objectivity.

Further, the Bayesian’s contention that she can delicately balance the racial factor in her calculations is refuted by recent discoveries about the psychological impact of stereotypes. A stereotype, unlike ordinary statistical information, radically alters our mindset, unconsciously bringing about a sea change in our perceptual readiness. Under the influence of a stereotype, we tend to see what the stereotype primes us to see. If violence is part of the stereotype, we are primed to construe ambiguous behavior as evincing violence, not on a retail but on a wholesale level. Thus, even if race marginally increases the probability that an “ambiguous” person is an assailant, decision makers inevitably exaggerate the weight properly accorded to this fact. Whatever merit there is to the contention that it is appropriate to consider a person’s race as one—just one—of the factors defining the “kind” of person who poses a danger, the racial factor assumes overriding psychological significance when the supposed assailant is Black.

For White Bayesians, cultural differences increase the danger of overestimating the threat posed by a supposed Black assailant. Nonverbal cues such as eye contact and body communication, for instance, vary significantly among subcultures, and thus may fail in intercultural situations.¹⁴ If the female bank patron in our opening hypothetical scenario were White (her racial identity is intentionally undefined), her misinterpretation of the Black victim’s eye and body movements as furtive and threatening may have resulted from cultural differences in nonverbal cues, illogically distorting her perception of danger.

Even if we accept the Bayesian’s insistence that his greater fear of Blacks results wholly from unbiased analysis of crime statistics, biases in the criminal justice system undermine the reliability of the statistics themselves. Racial discrimination in sentencing, for example, causes arrest statistics to exaggerate what differences might exist in crime patterns between Blacks and Whites, thus undermining the reliability of such statistics.¹⁵ A 1996 New York State study revealed that 30 percent of Blacks and Hispanics received harsher sentences than Whites in New York for comparable crimes, and that approximately four thousand Blacks and Hispanics are incarcerated each year for crimes under circumstances that do not lead to incarceration for Whites. Further exaggerating differences between Black and White crime rates is discrimination by police officers in choosing whom to arrest.¹⁶ Thus, although the rate of robbery arrests among Blacks is approximately twelve times that of Whites, it does not necessarily follow that a particular Black person is twelve times more likely to be a robber than a White.

Although biases in the criminal justice system exaggerate the differences in rates of violent crime by race, it may, tragically, still be true that Blacks commit a disproportionate number of crimes. Given that the blight of institutional racism continues to disproportionately limit the life chances of African Americans, and that desperate circumstances increase the likelihood that individuals caught in this web may turn to desperate undertakings, such a disparity, if it exists, should sadden but not surprise us. As Guido Calabresi, former dean of the Yale Law School and current federal appeals court judge, points out: “[O]ne need not be a racist to admit the possibility that the stereotypes may have some truth to them. I don’t believe in race, but if people are treated badly in a racist society on account of an irrelevant characteristic such as color or language, it should not be surprising if they react to that treatment in their everyday behavior.”¹⁷

The media spin on the comments of the Reverend Jackson decrying Black-on-Black crime used Jackson’s call for Blacks to take action on crime in their communities as an admission by the civil rights leader that racism and economic injustice have nothing to do with the crime problems of those communities. Columnist Mike Royko, for example, reported that Jackson believes it’s “a waste of time to expect government to reduce … urban mayhem.”¹⁸ From this standpoint, self-help and government investment are mutually exclusive. Anyone advocating antibias programs or federal aid to cities is portrayed as “making excuses” for Black people’s own self-destructiveness. Accordingly, when Jackson expressed fear that his ideas would be misconstrued by media and politicians looking for scapegoats, and further reiterated his long-standing insistence that both government help and self-help are needed for the African American community, he was widely derided. “[R]ather than grant [Whites the absolution for which they hungered]—and reap the enormous good will and political cooperation such a move might bring—Jackson has pulled back,” declared U.S. News.¹⁹

To the extent that Blacks do commit disproportionate numbers of violent street crimes, socioeconomic status largely explains such overrepresentation. Crime rates are inextricably linked to poverty and unemployment. Genetic explanations of crime statistics founder on the fact that crime and delinquency rates of the African American middle class are virtually identical to those of Whites similarly situated.²⁰

Recognizing the socioeconomic factors that drive violent street crime, the Bayesian may insist that he views race merely as a proxy for information with admittedly greater predictive value—such as income, education, and prospects for the future—but that costs more to obtain. “Thus,” says the Bayesian, “I consider a wealthy Black graduate of the Harvard Law School who is making six figures at a major Wall Street law firm to pose a lower risk of armed robbery than a poor and illiterate White high school dropout with little hope of gainful employment.”

“However,” he continues, “ascertaining an individual’s schooling and income may require a personal interview and reference checks. The costs of obtaining such particularized information may be prohibitive in many situations. Surely you can’t expect shopkeepers, cabdrivers, or people in the position of our hypothetical bank patron to incur such costs, to get a person’s life story before he fingers his buzzer, stops his taxi, or uses deadly defensive force against a ‘suspicious’ person. When obtaining such information is prohibitively costly, we must economize by using stereotypes and playing the odds.”

Viewed in this light, the Bayesian’s claim that race can serve merely as a proxy for socioeconomic status might seem persuasive. But if race is a proxy for socioeconomic factors, then race loses its predictive value when one controls for those factors. Thus, if an individual is walking through an impoverished, “crime-prone neighborhood,” as the Reverend Jackson may have had in mind, and if he has already weighed the character of the neighborhood in judging the dangerousness of his situation, then it is illogical for him to consider the racial identity of the person whose suspicious footsteps he hears. For he has already taken into account the socioeconomic factors for which race is a proxy, and considering the racial identity of the ambiguous person under such circumstances constitutes “double counting.”²¹

Since our hypothetical scenario takes place in a predominantly White upper-middle-class neighborhood, it does not seem to implicate the double-counting problem. Further, the discussion shall proceed on the basis of two assumptions: first, that the rate of robberies is “significantly” higher for Blacks than for non-Blacks; second, and most unrealistic, that the defendant’s greater fear of Blacks results entirely from his analysis of crime statistics. Given these assumptions, what objections to the argument of the Bayesian remain? Surely admitting statistics, carrying logic and objectivity on the rising and plunging curves of their graphs like Vulcans on dolphinback, better promotes the accuracy, rationality, and fairness of the fact-finding process than not admitting them.