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By Frederick Schauer
This booklet employs a cautious, rigorous, but vigorous method of the well timed query of no matter if we will justly generalize approximately participants of a bunch at the foundation of statistical traits of that workforce. for example, should still an army academy exclude ladies simply because, on general, girls are extra delicate to hazing than males? should still airways strength all pilots to retire at age sixty, even supposing so much pilots at that age have very good imaginative and prescient? Can all pit bulls be banned as a result competitive features of the breed? And, so much controversially, should still executive and legislation enforcement use racial and ethnic profiling as a device to struggle crime and terrorism? Frederick Schauer strives to investigate and unravel those prickly questions. whilst the legislation "thinks like an actuary"--makes judgements approximately teams in line with averages--the public profit should be huge, immense. nonetheless, profiling and stereotyping could lead on to injustice. and plenty of stereotypes are self-fulfilling, whereas others are easily spurious. How, then, will we make a decision which stereotypes are actual, that are distortions, which might be utilized rather, and for you to bring about unfair stigmatization? those judgements needs to depend not just on statistical and empirical accuracy, but additionally on morality. Even statistically sound generalizations might occasionally need to yield to the calls for of justice. yet vast judgments should not regularly or perhaps frequently immoral, and we must always now not continually brush off them as a result of an instinctive aversion to stereotypes. As Schauer argues, there's reliable profiling and undesirable profiling. If we will successfully make certain that's which, we stand to realize, now not lose, a degree of justice.
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Additional info for Profiles, Probabilities, and Stereotypes
Sample text
As a result, it is often necessary for lawmakers, even if not for ship captains, to legislate for the herd. The most noteworthy feature about the Stranger’s claim, however, lies not in the way that legislation differs from the behavior of the ship captain. Nor is it even in the way that legislation prescribes for all on the basis of what is best for most, for this too is by now familiar terrain. Rather, what is particularly important for us here is the Stranger’s argument that when there is an opportunity to avoid the error caused by the application of the universal and simple rule to some particular case, it would be “absurd,” “evil,” a “disgrace,” and an “injustice” not to avoid the error.
Indeed, we will return to it repeatedly in considering when, if at all, it is appropriate to make decisions on the basis of even statistically sound generalizations. It is possible, after all, that a reluctance to employ even statistically sound generalizations will helpfully decrease the reliance on statistically unsound and dangerous generalizations. Nevertheless, our primary focus here will be on the statistically sound generalization, the generalization constructed around a trait or property introduction that is relevant for the purpose at hand.
Is such a policy particularly unjust to those women of eastern European Jewish origin—well over 90 percent of them—who do not themselves actually possess any of the troublesome genetic mutations and thus do not themselves have any increased risk whatsoever of contracting breast or ovarian cancer? Many people would believe that imposing insurance restrictions on all women with this background just because of a genetic variation that appears in only some of them is indeed unjust. But what are the reasons for such a belief?