November 18, 2010
Racial Profiling To Limit Terror Attacks Is Fundamentally Flawed
Stop using racial profiling, says Professor William Press from the University of Texas at Austin. He claims that as well as being politically and ethically questionable, racial profiling does no better in helping law enforcement officials in their task of catching terrorists than standard uniform random sampling techniques. This is the topic of a paper publishing today in Significance, the magazine of the Royal Statistical Society and the American Statistical Association.
Racial profiling rests on the idea that people from particular racial or ethnic groups are more likely to be involved in acts of terror than people from other groups. The theory then suggests that law enforcement officers should spend a greater proportion of their time scrutinising people from the 'high risk' group. One problem with this approach is that innocent people who also belong to the targeted group rapidly become offended, and some may even become radicalised as a result.
In the Significance paper Press, based in the departments of Computer Science and Integrative Biology at the University of Texas at Austin, takes a thorough mathematical and statistical view of the process that underlies racial profiling, and concludes that some forms of racial profiling may even result in a smaller chance of detaining a terrorist than carefully conducted standard sampling.
In a world threatened by terrorists from a small number of countries, it is tempting to think that racial profiling for security purposes, even if morally objectionable, might save lives. "But uniform sampling, without the use of profiling, is surprisingly good. It is robust against false assumptions, it is deterrent, it is easy to implement, it is about as effective as any real-life system can be "“ and it is devoid of moral and political hazard," says Press.
He believes that the choice between a strategy of profiling and one of uniform random sampling should not be viewed as difficult; uniform random sampling wins.
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