Disparate treatment
This paper analyzes how difficult it is to measure disparate-treatment discrimination by the police. It also considers how existing researcher tools can create headway on such problems and how to solve them. The absence of data on the citizen conduct on policing decisions has made it hard for researches to analyze this data. Many crimes are not reported making it unobservable, especially for researchers. One of the methods that are best for analyzing this data is auditing. It will employ undercover officers that are of different races to elicit possible interactions with the police. Auditing presents safety, legality, and efficacy in the way policing is applied. There are many reasons why it is essential to measure disparate treatment. Some of the reasons are as the following.
- Racial profiling and constitutional doctrine- people ask why there is a need for reasonable empirical estimates of racially disparate treatment by officers. The reason is that the existence of different governmental treatment is the central question posed by current equal protection doctrine.
- It is hard for litigants to prove racial profiling.
- The use of racial classifications may be appropriate if they are invoked for a purpose that helps to promote substantive equality.
- The other reason is that racially disparate treatment adds a substantively meaningful dimension of harm as well as a distinct target for policy interventions.
Distinguishing the estimation of disparate treatment from other objectives of policing disparity is one of the best ways of analyzing information on disparate data. There are several things a researcher must balance when doing the auditing, especially of policing. They include safety, legality, importance, methodological rigor, statistical power, and the cost involved.
Some of the outcomes of analyzing disparate treatment are that it enables sound causal inferences. This data can help in the creation of more ambitious surveys about behavior and police-citizen contacts, especially in a country that has people of different colors like the U.S.