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4. Responses to the researchers’ report results

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4.1. “Proof” of racial profiling

In response to the researchers’ findings, the Ottawa Police Service and others have asserted that the data does not “prove” racial profiling. This raises a question about the value of the data and what it can tell us.

The purpose of the study was to assess whether racialized or Indigenous groups are over-represented in traffic stops, to provide clear evidence that the Ottawa Police Service, and others, could act on. The study met this goal. The researchers noted that the purpose of the study was not to prove causation.

In addition, generally speaking, racial profiling is not something that can be definitively proven through a quantitative study alone. Over-representation of racialized and Indigenous persons in police stops provides strong circumstantial evidence of the existence of inequitable practices. Courts and tribunals have accepted that racial profiling can rarely be identified by direct evidence; it will more often be proven by circumstantial evidence and inference.[29]

The high disproportionalities found in this report are strong circumstantial evidence of the existence of some form of racial profiling. The findings are reinforced by the large body of evidence that has flagged racial profiling as a major concern in policing throughout Canada and the U.S. Social scientific research, both quantitative and qualitative, tells of racial profiling of various types in policing.[30] It is routine to hear of complaints of racial profiling about police from members of Indigenous, Black and other racialized communities. The OPS traffic stop data collection initiative is the product of the settlement of one such human rights complaint. In addition, many recent human rights cases before tribunals and the courts in Canada have found racial profiling to be behind police actions with racialized and Indigenous peoples. 

Given these factors, these results still form a basis the Ottawa Police Service and others should act on to implement strategies to eliminate racial profiling.

4.2. Police deployment 

Other factors do not provide a conclusive non-discriminatory explanation for the finding that racialized people experienced disproportionately high incidences of traffic stops. For example, it has been suggested that the greater police presence in “high-crime” areas can account for greater proportions of Black and Middle Eastern people being traffic-stopped. The OPS has indicated that residents in areas with high crime want police to be active and visible, but this cannot be used by police to justify stop practices that have a disparate impact on racialized people.

One explanation given for racial disparities in stop and search studies is that greater police presence and attention is assigned to certain “hot spots” where the majority of crime is alleged to occur.[31] Because these neighbourhoods are often socio-economically disadvantaged and have many racialized residents, this greater police attention can in turn lead to disproportionate stops of racialized people.

However, in the OPS data, the vast majority of traffic stops were made because of perceived traffic violations or municipal offences, and not because the police suspected people of being engaged in criminal or suspicious activities.

In addition, as described earlier, a police deployment strategy that leads to greater traffic stops for racialized people in “high crime” areas may itself be a form of systemic racial profiling. Greater numbers of traffic patrols in racialized neighbourhoods means that racialized people are more likely to be targeted for minor offences, such as traffic offences, compared to White people in other neighbourhoods who may be committing the same offences. This can create an adverse effect based on race.

4.3. Perceiving race before the stop 

According to the study findings, in almost 89% of cases, OPS officers recorded that they did not perceive the race of the driver before the stop. This finding may create the inference that, if the race of the driver was not perceived prior to the stop, racial profiling in traffic stops could not have taken place.[32]

There is some reason to question the accuracy of the 89% figure. Two researchers did an independent study and interviewed 57 OPS officers about their experiences with the project. In their interviews, some front-line officers reported that they sometimes deliberately entered inaccurate race data for fear of how the data might affect their employment.[33] Many officers said that they “did not perceive” the race of the driver because of concerns that they would be implicated by the data.[34] Research suggests that police may record race-based data inaccurately or leave out data for various reasons.[35] Depending on the level of inaccuracy, this may lead to an underestimation of how often racialized drivers are stopped or biased findings.[36]

Further, even if in 89% of cases, officers did not observe the race of the driver prior to the stop, race may have been perceived, even implicitly, due to other factors. The characteristics of certain vehicles may give rise to a perception of who is being stopped, even if the race of the driver cannot be seen. Older vehicles or vehicles that have tinted windows, extra-large rims, or large sound systems may be used as proxies for the race of the driver.[37] Running a license plate can also provide information about a person, such as their name, that may give rise to a perception of the person’s race. For these reasons, it is unclear whether the 89% figure is a true indicator of whether the driver’s race was not perceived before the stop.  

More study is warranted of the 11% of stops where officers recorded that they could perceive the race of the driver. It is unclear if the same disproportionality between racial groups exists in this 11% as it does in the overall results. 

Even if the vast majority of officers did not perceive the race of the driver before the stop, this finding cannot adequately explain the fact that racialized groups experienced disproportionately high incidences of stops. In many cases these racial differences were quite high. In some cases, the differences could be explained by individual officer bias, whether implicit or explicit. However, the results are more likely explained by systemic racial profiling, which does not always require an officer to perceive the race of the driver before the stop. Traffic patrols in “high-crime” neighbourhoods, for example, may lead to more racialized people being stopped, even if the police officer does not perceive the race of the driver prior to the stop.

[29] Brownsupra note 6 at para. 44; Phipps, 2012, supra note 4 at para. 34; McKaysupra note 7 at para. 125; Pieterssupra note 4 at para. 72; Peartsupra note 4 at para. 95.

[30] This research is described further in section 5.

[31]Jose Torres “Race/Ethnicity and Stop and Frisk: Past, Present and Future.” (2015) 9 (11) Sociology Compass 931.

[32] Although this study dealt primarily with traffic stops and outcomes, racial profiling can happen at any point in an encounter with police: surveillance, stops, searches, arrests and other outcomes.

[33] Foster, Jacobs & Siu, supra note 1 at 54.

[34] Many officers reported this to the Ottawa Citizen. Shaamini Yogaretnam, “New rights hearing ordered in 2005 racial profiling case” Ottawa Citizen (19 June, 2015) at A4.

[35] These include indifference, hostility to collecting data and reporting, a belief that racial profiling is good police work, or concerns that data that show racialized drivers are stopped disproportionately are easy to misinterpret. Richard J. Lundman, “Race and Ethnicity Missingness in the Traffic Stop Data Reported by 308 Massachusetts Police Agencies” (2012) 2(1) Race and Justice  42.

[36] Ibid., Institute on Race and Poverty, Minnesota State Wide Racial Profiling Report: All Participating

Jurisdictions (Minneapolis: University of Minnesota Law School, 2003) at 31.

[37] See Alyson A. Grine & Emily Coward, Raising Issues of Race in North Carolina Criminal Cases (Chapel Hill, NC: UNC School of Government, 2014) online: UNC School of Government, (retrieved November 8, 2016) at 2-33 – 2-34.

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