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CSUS Race and Gender in Public Employment and Education Discussion

CSUS Race and Gender in Public Employment and Education Discussion

CSUS Race and Gender in Public Employment and Education Discussion

Question Description

Steps:

  1. Read over the ballot initiative provided on Ballotpedia. Provide two arguments supporting the proposition and two arguments against the proposition. (We will not give points if you copy/paste from the article or include direct quotes; read over the arguments provided and rephrase them in your own words.)
  2. Assume that without Proposition 16 there is no way for college admissions officers to determine the race of applicants (assume they cannot figure out race by name, country of origin etc.). Suppose that college admissions officers have taste-based discrimination against Asian students. How would you have expected Proposition 16 to affect the admission of Asian students at Sacramento University, if it had passed? Answer the same question under the assumption that instead college admissions officers statistically discriminate in favor of Asian students (that is, they assume that the expected GPA for Asian students is higher than for students of other races). Explain your answers.
  3. Suppose that Sacramento University mechanizes the admissions process by designing an algorithm that predicts which students will have high GPAs using four observable characteristics: SAT score, high school GPA, parents’ highest educational attainment, and native English speaker status. Suppose that each of these factors has a positive impact on expected GPA (i.e. native English speakers have higher average GPAs than non-native speakers, students with parents who have PhDs have higher average GPAs than students with parents who didn’t graduate high school, etc.) Under this system colleges will only admit students who are predicted to have a GPA above a certain threshold. Would you expect the racial composition of Sacramento University to be like that of California?1 Why or why not? Do algorithms always remove racial bias in decision-making processes? Refer to another article you read for class in making your argument.

GRADING

2 pts: first argument in favor of prop 16
2 pts: second argument in favor of prop 16

2 pts: first argument against prop 16

2 pts: second argument against prop 16

2 pts: if taste-based, what happens

2 pts: taste-based explanation
2 pts: if statistical, what happens

2 pts: statistical explanation

2 pts: will Sacramento university race = CA race?

2 pts: why or why not?

2 pts: do algorithms always solve our problems?

2 pts: cite algorithm paper

2 pts: formatted correctly
2 pts: within 500-700 words (including headers, footnotes, works cited)

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