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Mitigating Bias & Discrimination

Mitigating Bias


Acknowledging our own biases is a crucial step in mitigating bias, but it also presents one of the greatest challenges in self-discovery and self-understanding because it forces us to look directly at our own flaws. However, it helps us to better understand how we may be hurting ourselves and those around us. Curious about your own biases? Take the Harvard Implicit Bias Test.

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Reducing Bias in the Workplace


There are many steps that employers and employees can take to reduce bias at work. Building a diverse workforce is a key component, but it is only the start. To reduce bias in the workplace consider the following:

  • Understand how implicit (and explicit) biases affect the workplace including:
    • Affinity bias: unconscious tendency to get along with others who are like us.
    • Halo effect: a cognitive bias in which our overall impression of a person influences how we feel and think about their character.
    • Perception bias: tendency to form simplistic stereotypes and assumptions about certain groups of people.
    • Confirmation bias: tendency to interpret new evidence as confirmation of one's existing beliefs or theories.
    • Groupthink: the practice of thinking or making decisions as a group in a way that discourages creativity or individual responsibility.
  • Be able to identify your personal implicit biases.
  • Update hiring practices including:
    • Diversify the hiring committee
    • Create clear compensation policies 
  • Commit to cultural awareness training.
  • Commit to Implicit bias training.
  • Begin and/or evaluate mentoring programs.

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Mitigating Human Bias in AI


"AI has the potential to help humans make fairer decisions - but only if we carefully work towards fairness in AI systems as well" (Silberg & Manyika, 2019). AI has become more prevalent in our society, but like most technology, it is only as good as the humans who create it. Therefore, if humans have implicit biases the AI will too. The resources below discuss this issue and ways to reduce bias in AI.

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Reducing Bias in Healthcare


According to the Center for American Progress, "The United States is home to stark and persistent racial disparities in health coverage, chronic health conditions, mental health, and mortality" (Carratala & Maxwell, 2020). Their fact sheet on Health Disparities by Race and Ethnicity contains data covering many areas such as health insurance coverage, chronic conditions, and access to mental healthcare. The image below is from a CDC report on prgnancy-related deaths. The report showed that Black women have a higher mortality rate than other races. Visit the resources below to learn more about bias in healthcare and how it can be addressed.

During 2007–2016, black and American Indian/Alaska Native women had significantly more pregnancy-related deaths per 100,000 births than did white, Hispanic, and Asian/Pacific Islander women.

Source: CDC. Racial/Ethnic Disparities in Pregnancy-Related Deaths — United States, 2007–2016

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References


Carrratala, S. & Maxwell, C.. (2020, May 7). Health disparities by race and ethnicity. Center for American Progress. https://www.americanprogress.org/issues/race/reports/2020/05/07/484742/health-disparities-race-ethnicity/ This link opens in a new window