Implicit Bias and First Responders: 5 Common Misperceptions
Lois James, PhD
Bias is our conscious or unconscious preferences for and against certain things, places, people or groups. If we are aware of and actively “claim” our bias, it is explicit. For example, if someone was to say “I believe that drug addiction is a sign of moral weakness,” they would hold an explicit bias. Implicit bias, on the other hand, typically operates outside of our conscious awareness. Or, if we are aware, we would likely not admit to having them. For example, although many people would vehemently deny it, implicit associations between Black Americans and threat are very common in America (Xu, Nosek and Greenwald, 2014).
At its most basic level, bias is a survival heuristic, evolved from the ancestral environment where it was beneficial to fear people who looked different to you as they were likely not part of your tribe (James, Todak and Savage, 2020). In the modern environment it’s no longer beneficial to fear “out groups,” and yet there are still plenty of influences that help form our implicit biases: family, peers, teachers, mentors, community groups, media, and so on. Bias within policing, and in particular racial bias, has become a leading player in the national conversation about public trust in police legitimacy (Nix and Pickett, 2017). Although this conversation is critically important, several misperceptions about bias exist that must be addressed for true progress to be made.
1: “Having implicit bias is the same thing as being racist.”
One of the most common misperceptions about bias is that people tend to equate implicit bias with racism. Let’s be very clear: Everyone has implicit bias. We have developed to process information using cognitive shortcuts, which includes pattern matching based on our associations between different things. In this way, implicit bias is a key part of how our brains filter information (Greenwald and Krieger, 2006).
Racism, on the other hand, is prejudice, discrimination or antagonism to a group based on their race or ethnicity (Miles and Brown, 2003). That doesn’t mean that implicit bias cannot lead to racism; nor does it mean that racism is always conscious. Implicit bias that influences our decision-making outside of conscious awareness can lead to discriminatory behavior targeting a particular racial group, which by definition is racism. But having implicit bias associating Black Americans with threat, for example, does not necessarily lead to racism, providing that the person holding the implicit bias does not behave in discriminatory or prejudiced ways. This is a core message within implicit bias training—that having implicit bias does not have to influence behavior if you are aware of what your “mental filter” looks like so that you can recognize errors in your mental “shortcuts” and safeguard against discrimination such as racism (James, 2018).
2: “Only cops are biased, not members of the general public.”
Another common misperception is that police officers have a monopoly on implicit bias, when in fact it is universal due to its evolutionary routes in survival. This means that developing biases based on our associations between different things is hardwired into us, regardless of who we are. The Implicit Association Test (IAT) is a way of measuring implicit bias. IATs exist for many different types of bias and IAT data has been collected extensively from the general public in the United States. Results show that a majority of Americans have a preference for white people over Black people, young people over old people, able bodied people over disabled people, straight people over gay people, and typical weight people over overweight people.
Research has been conducted specifically on police officers and has found that they are certainly not immune to implicit bias. One study found that 96% of officers held associations between Black Americans and threat (James, James and Vila, 2016). That same study, however, showed this implicit bias did not predict behavior in simulations featuring white, Black, and Hispanic citizens. It’s possible that officers are more likely to develop implicit biases based on their work experiences. For example, if an officer works in a high-crime, low-income, predominantly Black neighborhood, they will likely develop implicit associations between Black people and threat; whereas if an officer works in a low-crime, affluent, predominantly Black neighborhood they are much less likely to develop that same implicit bias (at least from their work-related exposure).
For law enforcement officers, the risk of developing implicit bias can have extreme consequences. It’s therefore imperative they learn how to prevent theses biases from affecting their interactions with the public.
3: “Having good intentions is enough to avoid being biased.”
Nobody likes to be blamed for something they don’t feel is “their fault.” However, just because we don’t intend to cause harm doesn’t necessarily mean we don’t cause harm. However well intentioned, we are responsible for our actions. Recall that implicit bias often operates outside of conscious awareness: We can behave in discriminatory or prejudiced ways without intending to (or even being aware that we have). We have come to the point in society—especially in the aftermath of the civil unrest following the multiple unarmed Black people who died at the hands of police in 2020—where we must be responsible for the impact we have, not just the impact we intend to have. We must all do the work to recognize in ourselves what our implicit biases are, how we see the world and the people in it, and how we use this mental filter to guide our behaviors.
4: “There’s nothing that I can do to fix systemic problems.”
One misperception people have is that deep-rooted issues such as institutional or systemic discrimination are too big for any one person to tackle. Systemic discrimination entails systems and institutions discriminating against certain groups of people. The clearest example is systemic racism, which permeates housing, education, healthcare and criminal justice systems (Feagin, 2013). Each of these systems were set up by white men to benefit white men. That is not to lay blame, simply to explain how practices that favor the majority tend to evolve.
Within the criminal justice system, an example of systemic racism is the discriminatory sentencing based on risk prediction algorithms. Ironically, these algorithms were designed to be fair and equitable and reduce bias by taking some of the decision-making away from judges. However, the information that feeds these algorithms (despite not containing race) contain many proxies for race (such as neighborhood) that lead to higher recidivism risk predictions and thus longer sentences for Black people than white people for very similar or the same offences (Johndrow and Lum, 2019). Even if this disparity was not the consequence of biased intentions, the bottom line is that these algorithms do result in discrimination against Black people. And just like we are responsible for our impact and not just our intentions, if a system was not designed to the discriminatory, but it turns out to be, then it must be addressed. Systems are made up of individuals, and individual voices—even if constrained by systemic factors—can ignite or propel change.
5: “The public is always going to think officers are racist, so what’s the point?”
In many of the implicit bias training sessions with police officers I’ve conducted, a typical misperception voiced is that the public are never going to appreciate them or “change their minds” about racism within policing. This understandably can lead to a great deal of hurt and defensiveness, especially for well-intentioned officers who genuinely try to connect with the public. However, within these training sessions, when probed, almost every officer who holds this defeatist outlook can actually describe multiple situations in which they did in fact change someone’s mind, helped or left a person with a better perception of the police profession. Cognitive behavioral therapy teaches that humans are very likely to focus on negatives rather than positives and are prone to errors such as mind reading, projecting and overgeneralizing (Fenn and Byrne, 2013). Sometimes a shift in mindset is required, because a defeatist attitude is universally not helpful and very likely unfounded.
These five misperceptions around bias are a handful of many but are important to address in order to have open and honest discourse about bias. The goal is to find ways to move forward and mend damaged relationships. Following are key recommendations:
- Learn to recognize implicit bias within yourself, what your “mental filter” through which you process information looks like and identify associations that you have between different things.
- Remember that you are responsible not just for your intentions, but also for the impact of your behavior or decisions.
- Understand that bias is universal, so blame for implicit bias is pointless; focus on accountability for actions.
- If systems are discriminatory—even if they were not designed to be—it is our responsibility to fix them.
- Believing that nothing will ever change is unfounded and unhelpful, and each of us is responsible for fighting against discrimination and prejudice.
Feagin, J. (2013). Systemic Racism: A Theory of Oppression. Routledge.
Fenn, K., and Byrne, M. (2013). The Key Principles of Cognitive Behavioral Therapy. InnovAiT, 6(9), 579 – 585.
Greenwald, A. G., and Krieger, L. H. (2006). Implicit Bias: Scientific Foundations. California Law Review, 94(4), 945 – 967.
James, L., Todak, N., and Savage, J. (2020). Unnecessary Force by Police: Insights from Evolutionary Psychology. Policing: A Journal of Policy and Practice, 14(1), 278 – 291.
James, L. (2018). The Stability of Implicit Racial Bias in Police Officers. Police Quarterly, 21(1), 30 – 52.
James, L., James, S. M., and Vila, B. J. (2016). The Reverse Racism Effect: Are Cops More Hesitant to Shoot Black than White Suspects? Criminology & Public Policy, 15(2), 457-479.
Miles, R., & Brown, M. (2003). Racism. Psychology Press.
Johndrow, J. E., and Lum, K. (2019). An Algorithm for Removing Sensitive Information: Application to Race-Independent Recidivism Prediction. The Annals of Applied Statistics, 13(1), 189 – 220.
Nix, J., & Pickett, J. T. (2017). Third-Person Perceptions, Hostile Media Effects, and Policing: Developing a Theoretical Framework for Assessing the Ferguson Effect. Journal of Criminal Justice, 51, 24-33.
Xu, K., Nosek, B., and Greenwald, A. (2014). Psychology Data from the Race Implicit Association Test on the Project Implicit Demo Website. Journal of Open Psychology Data, 2(1).