Cecily Zamora
Bias-based policing is a significant issue that can affect law enforcement decisions and, in turn, impact public safety. Implicit biases, which are unintentional prejudices, can influence officers' actions and decisions, particularly regarding race, gender, and economic status. Research and real-world data analyses have revealed biases against minorities in law enforcement practices, with Black people, for example, being more likely to be victims of police brutality. To address these issues, law enforcement agencies are implementing training and policy interventions to reduce implicit bias and promote fair and equal treatment for all individuals. While this is a challenging endeavour, understanding confirmation bias, statistical data, and the impact of personal experience and societal exposure can help mitigate biased policing and improve community trust and justice.
| Characteristics | Values |
|---|---|
| Implicit bias | The automatic association people make between groups of people and stereotypes about those groups |
| Racial bias | Black faces looked more criminal to police officers; police officers are more likely to mistakenly shoot unarmed black people |
| Gender bias | Stereotypes based on gender |
| Bias based on sexual orientation | Stereotypes based on sexual orientation |
| Bias based on national origin | Stereotypes based on national origin |
| Bias based on economic status | Stereotypes based on economic status |
| Clustering illusion | The failure to recognize statistical regression, e.g. believing a decrease in crime is due to a new law enforcement policy |
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Racial bias
For example, a 2014 investigation revealed substantial racial disparities in school discipline, with students of colour facing higher rates of out-of-school suspensions and police referrals, which were attributed to racial bias rather than more frequent or serious misbehaviour. Similarly, drug arrests and incarceration rates also reflect racial disparities driven by biased policies and bias in law enforcement. Over one million drug arrests were made in 2019, with Black individuals, who comprise 14% of the US population, accounting for 29% of these arrests. Even when accounting for drug use rates, which do not substantially differ by race, Black Americans were 3.6 times more likely to be arrested for marijuana possession than their white counterparts in 2018.
Furthermore, racial profiling and the "stop-and-frisk" practice have been widely discredited due to their reliance on racial stereotypes and biases. Despite efforts to implement reforms, such as diversifying police forces and implicit bias training, critics argue that these measures have had limited success in changing behaviours and reducing violent conduct against people of colour.
The persistence of racial bias in law enforcement is rooted in systemic exclusion, discrimination, and implicit and explicit biases. Addressing these issues requires comprehensive social change and a range of new approaches to law enforcement, courts, and community involvement. While biased associations are challenging to unlearn, research suggests that they can be gradually replaced with non-biased associations through training and policy interventions.
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Stereotypes
Research has shown that African Americans often experience stereotype threat, which is the fear of inadvertently confirming negative stereotypes about their group. For example, the stereotype that young African American males are more prone to criminal activity has been linked to disparate treatment by the police. This can create a self-fulfilling prophecy, where the belief in the "racist police officer" stereotype leads to increased support for forceful or threatening policing tactics, further reinforcing negative stereotypes.
Social psychologist Cynthia Najdowski's research suggests that the concern about being judged unfairly due to stereotypes can cause innocent black individuals to exhibit nervous behavior, which is often interpreted as a cue to deception by police officers. This, in turn, can increase the likelihood of investigatory contacts with blacks, leading to a disproportionate targeting of African Americans by law enforcement.
To address these issues, law enforcement agencies should provide training and policy interventions aimed at recognizing and mitigating biases. By challenging biased associations and replacing them with non-biased ones, it is possible to reduce the influence of implicit bias and promote fair and equal treatment of all individuals.
Furthermore, law enforcement officers should focus on specific, observable details when providing suspect descriptions, rather than relying on generalizations based on race, gender, or economic status. By utilizing legitimate law enforcement techniques and seeking exculpatory evidence, officers can ensure that investigations are conducted fairly and without bias.
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Confirmation bias
For example, investigators may develop a hypothesis about a suspect's guilt early in an investigation and then selectively filter evidence to support that hypothesis, ignoring or downplaying contradictory evidence. This can lead to tunnel vision, where investigators become overly focused on a single suspect or line of inquiry, potentially missing or disregarding important information that could exonerate the suspect or point to other leads.
To address confirmation bias, law enforcement officers should be trained to recognize and mitigate their biases. This includes understanding the concept of exculpatory evidence, which is evidence that clears a person of blame. By actively seeking out and considering exculpatory evidence, officers can avoid the confirmation bias trap and ensure that their decisions are based on all available evidence, rather than a selective interpretation that confirms their initial suspicions.
Additionally, policies and interventions can be implemented to reduce the influence of implicit biases. For example, changing the context of interactions, such as through community policing initiatives, can help to reduce the impact of racial stereotypes and promote fairer treatment of all individuals.
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Implicit bias
Research has shown that implicit bias can significantly influence the outcomes of interactions between police and citizens. For example, studies suggest that implicit bias contributes to "shooter bias", where police are more likely to shoot unarmed Black suspects than white ones, as well as the frequency of police stops for members of minority groups. Implicit bias can also impact the prioritization of cases, with public defenders prioritizing cases involving white defendants, which can have major impacts on communities.
Despite the challenges of addressing implicit bias, some efforts have shown promise. The work of Phillip Atiba Goff, President of the Center for Policing Equity, has demonstrated that implicit bias can be reduced through training and policy interventions with law enforcement agencies. Biased associations can be gradually unlearned and replaced with non-biased ones, and the influence of implicit bias can be reduced by changing the context of interactions.
However, psychological scientists have found that implicit biases are difficult to change and nearly impossible for the decision-maker to recognize, making training less effective. A more immediate approach to reducing racial disparities in policing is to decrease the frequency of actions most vulnerable to bias, such as constraining discretion in stop-and-search decisions, which has been shown to reduce the impact of racial bias.
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Decision-making processes
Research has shown that implicit bias can impact the decision to use deadly force, with simulations indicating that police officers are more likely to mistakenly shoot unarmed Black individuals. Analyses of real-world data have also found bias against minorities. For example, a study of 991 cases of individuals killed by police in the US in 2015 found that Black people were more than twice as likely as White people to be unarmed when killed by police.
To address implicit bias in decision-making processes, law enforcement officers can be trained to recognize and mitigate their biases. This involves ensuring that decisions are based on evidence and reasonable suspicion rather than stereotypes. For instance, when providing suspect descriptions, officers should focus on specific, observable details instead of relying on generalizations based on race, gender, or economic status.
Additionally, policy interventions can play a role in reducing implicit bias. For example, requiring officers to provide more detailed explanations for investigative stops can help identify and address biases. Community outreach initiatives, such as "Coffee with a Cop," also allow law enforcement officers and community members to interact outside of a law enforcement context, fostering long-term social contact that can reduce prejudice and discrimination.
Furthermore, understanding confirmation bias and statistical regression is crucial for law enforcement. Confirmation bias refers to the tendency to interpret new evidence as confirmation of existing beliefs or expectations. In the context of law enforcement, this may lead to officers interpreting evidence in a way that confirms their initial suspicions rather than objectively evaluating all available information. Statistical regression is a concept where extraordinary data are followed by a return to more average data points. In the context of crime rates, this means that a short drop in crime rates after a crime wave is not necessarily due to new law enforcement policies but is likely a result of natural fluctuations. Recognizing these biases can help law enforcement officers make more informed and unbiased decisions.
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Frequently asked questions
Implicit bias refers to any prejudice formed unintentionally and outside of a person's direct knowledge. It is influenced by personal experiences, societal attitudes, and cultural exposure. Research has shown that implicit racial bias can affect an officer's decision-making when using deadly force, with simulations indicating that police officers are more likely to mistakenly shoot unarmed Black people.
Training and policy interventions have been shown to help address implicit bias. For example, anti-bias training at the University of Wisconsin-Madison led to an increase in the hiring of female faculty in STEM departments. In the context of law enforcement, training can focus on critical thought and discussion about mechanisms of bias and identity traps. Long-term social contact with other groups is also considered an effective way to reduce implicit bias.
Bias-based policing refers to the influence of implicit bias on law enforcement decisions and actions. One example is racial profiling, where officers may be more likely to stop and search individuals based on their race or ethnicity. Another example is gender bias, where officers may provide suspect descriptions based on gender stereotypes rather than specific, observable details.
