It is no secret that employers face special challenges managing ever diversifying workforces.
Differences in race, sex, religion, age, etc. – and discriminatory or harassing acts based upon them – can lead not only to difficult human resources situations, but also potential legal liability. Municipal employers must deal with an additional element of uncertainty when their police, fire, zoning and public works personnel interact with the public.
These challenges are magnified when the concept of implicit bias is considered. Rather than overt biased acts – for example, not permitting a menorah to be displayed on an employee’s desk but permitting a Christmas tree – implicit bias is the mind’s subconscious shortcut to decision making. This is often based upon preconceived and often unrecognized assessments of individuals based upon known traits about those who are different from them. These biases can be based on any combination of factors including race, gender, national origin, sexual orientation and age and the effect of them on our overt acts is amplified in stressful situations when the brain needs to make quicker decisions based upon limited information. While biases themselves are not illegal, they can lead to inadvertent discriminatory acts. For example, the Las Vegas Police Department determined that officers involved in arrests following pursuits were more likely to use force against those that did not look like the officer. Following the institution of a policy that the pursuing officer’s backup would make the arrest whenever possible, there was a twenty-three percent drop in violent arrests as the less stressed backup was less likely to resort to implicit biases about the fleeing suspect.
While the implicit biases within a workforce are not likely to be changed in the short-term, recognizing the existence and effect of implicit bias on behavior is an important first step in reducing discriminatory acts that could result in liability for the employer. Employers are advised to include implicit bias in anti-discrimination training and to review discriminatory instances for patterns that might suggest recognition and mitigation of implicit bias which could reduce the frequency of discriminatory events or practices.
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