Week 5: Extra Credit/Implicit Bias

Week 5: Extra Credit

I ended up taking the transgender social aptitude tests of the Harvard University Project implicit simulation. My conclusion ended up being, “Your data suggest no automatic preference between Transgender People and Cisgender People” which is a very great result in my opinion. However, this doesn’t mean that I don’t have any implicit biases that do affect me. This is a fact that we have seen time and time again regardless of trying our hardest to be nonbias. In the Ohio State Study, “Understanding Implicit Bias” one of the first things it states and recognizes is, “Implicit biases are pervasive.  Everyone possesses them, even people with avowed commitments to impartiality such as judges”. After knowing this, it is important to acknowledge how those implicit biases can affect the onboarding and interview process for all different people. With that in mind, there needs to be different exercises or best practices in place to help diminish implicit bias and create a fair environment for all persons in the workforce. Over this course, we have learned many different ways to help accomplish a fair and equal recruitment process. One of the biggest overall tactics is to provide a structure to the whole process specifically with interviews because this is where meeting applicants and implicit bias really has the ability to take place. How structure can be created is diverse. This can be things like having the applicant talk to many people, creating almost an answer rubric for questions, and having the same relevant questions that allow for the applicants to show measures of predictive performance. These exercises add up and truly make a difference so personal bias is minimized and candidate validity can truly occur. 


Authors The Kirwan Institute. (2012, May 29). Understanding implicit bias. Understanding Implicit Bias | Kirwan Institute for the Study of Race and Ethnicity. Retrieved October 27, 2021, from https://kirwaninstitute.osu.edu/article/understanding-implicit-bias.

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