California’s Proposition 16 has a few advantages for Black people. It would have ended the ban on affirmative action, a measure initiated in 1996 (Brice et al., 2020). Proposition 16 would have increased the number of Back and Latino students due to the administration’s affirmative action to increase the diversity and support the underprivileged sections. Similarly, in employment, Proposition 16 might have improved the chances of jobs for racially disadvantageous groups. This might happen due to the organizations’ affirmative action. California’s Proposition 16 offers some important advantages for the racially disadvantageous groups in education and employment.
California’s Proposition 16 has two main disadvantages. In the presence of Proposition 16, a White person would have been unable to secure admission in a university because the seat might be captured by an underserving Black student. In the long run, the talented White people might not produce their best due to lesser accomplishments in education. The second disadvantage might be fewer job opportunities for White people. Black people and Latinos might get jobs they would not have got in the absence of Proposition 16. Proposition 16 offers two main disadvantages for White people in education and employment.
In the presence of Proposition 16 which encourages affirmative action, the admission officer might grant admissions based on taste-based discrimination. If the admission officer is biased against Asian students he or she will not give many admissions to Asians due to their selective taste against Asians. The officer might raise objections to low SAT scores and previous CGPAs of Asian students. Therefore, his or her taste-based decision against Asians would result in fewer Asians in the university.
Similarly, in the case of statistical discrimination, if the officer favors Asians, their biased thinking might work in the presence of Proposition 16. The biasness in favor of Asians might result in a higher number of Asians admitted to the university on the plea of affirmative action. The enrollment of Asian at UCSD was higher at close to one-third of total new intakes in the last fall as compared to their population in California. The admission officer might know the race and ethnicity of applicants; therefore, merit would not prevail instead biases would play their role in both the question’s scenarios.
The UCSD’s racial composition might not be like California’s. Undoubtedly, racial discrimination might go down in the admission process if the UCSD mechanizes the admissions procedures by designing the algorithm. Scientifically designed algorithms might reduce certain biases in the decision-making process. Since the referred algorithm gives higher weight to the parents being native English speakers, the population of White people might increase in the university. Similarly, higher SAT requirements might increase the number of affluent class students which is another form of discrimination. So algorithm might increase racial and other biasness.
In the presence of an advanced mechanized algorithm for the admissions, the UCSD race ≠ CA race because there is no such advanced mechanized algorithm to decide who is to live in California. UCSD race = CA race only if a similar mechanized algorithm decides who is to live in the state and who is to get admission to the UCSD. Algorithms might solve a plethora of human problems, but they are not an all-purpose pill and they cannot always completely remove racial biases in decision-making. According to experts, algorithms cannot solve all problems as it is not magic, and it has their own issues and faults (Polonski, 2018). Therefore, the racial basis might creep in due to the basis on which the algorithms are built for example higher proficiency in English which favors White people. Due to an absence of an algorithm to select the people living in California, the UCSD race ≠ CA; moreover, algorithms cannot eliminate all problems and all racial biases as they have their own inbuilt faults.
Brice, A. (2020, November 2). ASUC on California Proposition 16: Everyone benefits from greater diversity. Berkeley News. https://news.berkeley.edu/2020/11/02/asuc-on-proposition-16/
Polonski, V. (2018, May 25). Why AI can’t solve everything. The Conversation. https://theconversation.com/why-ai-cant-solve-everything-97022