This Case Study Analysis should be 3 pages long with a cover sheet and reference page (cover sheet and reference page do not count in the page minimum).
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▪ Introduction
Provide an overview of the case (1-2 paragraphs)

▪ Main body
Use the Discussion Questions at the end of the Case Study to guide you. This should be written as a narrative do not directly answer the questions in a Q&A format. Include in your analysis material covered in the textbook, supplemental material is a plus. Consider, assess, and clearly state at least three (3) points that defend your opinion. Identify and acknowledge positives and negatives of your position. Include supporting evidence from your sources, make sure to use proper APA in-text citations when warranted (3-5 paragraphs).

▪ Conclusion
Summarize main findings. Identify and justify the position you have taken (2-4 paragraphs).

▪ References
A list of sources referred to in correct APA format.

Case written by Deanna Malatesta, Associate Professor, O’Neill School of Public and Environmental Affairs, Indiana University

Elsa sat in the parking lot of an upscale condominium waiting for her friend to join her. Elsa’s friend did not drive, so each week Elsa waited in the same parking lot for her friend so that they could go shopping. On this particular day, Elsa noticed a man and two well-groomed children walking to a silver SUV, just a few spots away. The children quietly climbed into the vehicle; the man slid behind the wheel and they slowly drove off. Elsa thought she had seen the children at the condominium before, but she was sure that she hadn’t seen the man and thought he looked out of place. Elsa had an uneasy feeling.

The driver appeared to be between 20 and 30 years of age and Black. One of the younger children appeared to be about three or four years of age, and the other about seven. Both children were Caucasian. Elsa contemplated calling the police to report what she thought looked suspicious but did not want to have her friend come out only to see she had left. After pondering a minute, she decided her friend would have to wait—she could explain it all later.

Elsa exited the lot and caught up to the silver SUV just as it was turning out of the complex. Elsa decided to follow the car, which now was on the highway. As she followed the car, Elsa called 911 and explained the situation. “I am following a man and two children in a silver SUV, and the man looks very suspicious. I have a bad feeling about this. I don’t think he belongs with the children.” Elsa also told the police dispatcher, “it may be nothing, but I would feel just horrible if something happened to these children and I did not report it.” She gave the dispatcher a description of the vehicle, the vehicle license plate number, and her location.

Elsa continued to follow the SUV down the highway. About five more minutes into the drive, she witnessed a police car behind her. The police car pulled in front of her, put its siren on and motioned the silver SUV to pull over. Elsa also pulled over about 50 yards away, far enough not to interfere but close enough to be available in case the police needed to question her.

The officer exited the police car and approached the driver of the silver SUV. “Let me see your identification, registration, and insurance card please.” When the driver asked the officer why he was pulled over, the officer asked, “What is your name and where are you going with these children”? The driver responded. “My name is Steve Clark. I am their babysitter; I am taking them to my apartment to watch them until their mom comes home from work.” The officer asked, “Do you have the mother’s phone number”? Steve looked the number up on his cell phone and gave it to the officer. “Stay in the car and I will be back with you in a moment,” the officer said. Steve and the children waited in the car for about 15 minutes while the officer communicated on the radio with his station. Then another police car arrived. At this point the two police officers had a conversation between themselves. This went on for another 15 or 20 minutes before the first police officer approached Steve once again. With no explanation, the police officer handed Steve back his papers and told him he could leave. Steve asked once again, “Why was I pulled over?” The officer responded, “A woman called 911 and reported that she thought you looked suspicious. I am sorry for the inconvenience. You can go on your way, sir.” By this point Steve just wanted to leave. He was nervous and the kids were very unsettled. The younger child was crying. Steve pulled back on to the highway and went on his way.

To better understand the case, students can take an explicit bias test in about 10 minutes at Harvard’s Project Implicit Bias. In taking the test you will experience the phenomenon yourself. You can choose from many implicit bias types; gender, age, and race are just a few of the options. To receive credit for the test, please take a screen shot of the last page, without the score. This is easily posted on Canvas. The test is available at https://implicit.harvard.edu/implicit/.

Questions

1. Do you think that Elsa thought of herself as a racist?
2. Do you think what happened to Steve would have happened if Steve had been white?
3. What stereotypes were invoked in this case and by whom?
4. How did stereotyping influence and or frame the situation for Elsa?
5. How did stereotyping influence and or frame the situation for the police?
6. What is your opinion about Elsa’s reaction to seeing Steve and the two children?
7. Would training on implicit bias have a beneficial impact? For whom?

8. According to the New York Times best-seller by Cathy O’Neill (2016), “Weapons of math destruction: How big data increases inequality and threatens democracy” using “big data” and some mathematical models can threaten the very fabric of democracy. How might implicit bias be a problem in using big data to make “democratic” decisions? Please give examples cite sources from peer-reviewed journal(s).

References and Additional Resources

Mahzarin, R. Banaji, and Anthony G. Greenwald. Blind Spot: Hidden Biases of Good People Delacorte Press, 2013.

O’Neil, Cathy. Weapons of math destruction: How big data increases inequality and threatens democracy. Crown, New York: Broadway Books 2016.

Payne, Keith, Laura Niemi, and John Doris. “How to Think about Implicit Bias. Amidst a Controversy, It’s Important to Remember That Implicit Bias Is Real—and It Matters.” March 2018. Available at https://www.scientificamerican.com/article/how-to-think-about-implicit-bias/