By: Christopher Clark
This article is about New York’s “Stop and Frisk” police policy. The police and others contend the policy is permissible because it is done when only when police have observed suspicious behavior. However, opponents don’t like that these searches completely rely on the judgment of the police officer on the ground. In effect, this judgment is heavily influenced by racial and other stereotypes, as seen by the statistics on those stopped. Here, the Times reports on police officers’ perspective on the Stop and Frisk. Even if the police are being honest that they aren’t letting racial stereotypes enter into their decisions, these prejudices also operate on a subconscious level. The question becomes could this factor be vetted empirically and given an appropriate weight (Zocalo drug dealer case).
Related to Monday’s class, Stop-and-Frisk is a variant of pattern-based searching using government law-enforcement data legally collected (i.e. the police officer’s observations from roaming about in public). Instead of this data being run through an empirically-validated algorithm to see which data gives rise to a heightened suspicion, the data are simply analyzed by police who run their own algorithm in their head. This algorithm is going to be different for every police officer, and is also going to be intrusive for the people who are frisked—both red flags when it comes to data mining (although this data is not in a digital format). This might be one example where a more standardized, mechanized decision would create less problems than individual judgment.
Related to Wednesday’s class, the Supreme Court has ruled that this type of “Stop and Frisk” procedure is not a traditional “search” requiring a warrant, instead accepting a lesser showing of suspicion (Terry v. Ohio). This is one policy where the limitations of the 4th Amendment in protecting against government intrusion are evident.