Step 1: Pro/con Arguments
The main argument presented by the proponents of Proposition 16 is that it will eradicate the deep-rooted discrimination in the state by providing equal opportunities to the public while ensuring diversity in all public sectors. Furthermore, affirmative actions taken by the government under Proposition 16 will provide good jobs, equal wages, and education to all Californians and remedy racism and gender bias to build a California that will truly reflect the shared values. Proposition also does not impact the current fiscal policies and other constitutional laws that prohibit unlawful discrimination.
However, the opponents argue that this attempt is primarily implementing one kind of discrimination to end others and, therefore, does not hold any practical importance. Moreover, it will provide the state administration constitutional rights to impose discrimination and preferential treatment based on sex, race, color, ethnicity, or national origin that will finally result in unequal access to opportunities, devaluation of merit, legalization of discrimination, and ethnic differences among different groups.
Step 2: Prop 16 and Asian Student Admission
Since Proposition 16 allows affirmative actions, the admission officers having taste-based discrimination against Asian students can negatively influence the admission of Asian students at UCSD. As taste-based discrimination does not incorporate any statistical data and behavioral uncertainty to support the relative preferences of individuals, the prominent effect of the discrimination against Asian students will be their decreased admissions and low employment rate, thus, creating inequality in society. Therefore, many research studies show that minority admissions in the UCs have increased significantly since Prop. 209 passed which has resulted in improved diversity in universities and the workplace. So, in the absence of any alternatives to determine the race of applicants, the taste-based discrimination of admission officers would result in decreased admissions of Asian students.
In the case of statistical discrimination, the college admissions officers can again take affirmative actions based on their preferential treatment to favor the Asian students’ admissions to the UCSD. The certain consequences in this regard will be the improved admission ratio of Asian students while admissions of other races such as African Americans, Native Americans, and Latin Americans will sufficiently decrease. The data shows that the enrollment of Asian at UCSD was proportionately higher at 30% in the last fall as compared to their population in California at 15%. The statistical discrimination will cause admission officers to use their perceived limited information about Asian students erroneously which will exacerbate discrimination.
Step 3: Algorithms
The racial composition of UCSD will not be the same as that of California if admissions are not directly based on racial discrimination and affirmative actions but on projected GPA which involves independent variables such as SAT score and high school GPA etc. However, the language status does involve racial discrimination that may influence the admission of a particular race to UCSD despite its positive effect on the GPA. Also, the projected GPA of students cannot necessarily determine the probability of their success in the university. But it can lead to class discrimination and underrepresentation of minorities being based on SAT, GPA, and parent’s educational status. On the other hand, the racial composition of California will deviate significantly from the racial composition of UCSD by considering the demographic and socio-economic situation of the state.
Moreover, the algorithm itself cannot remove the biases in the admission process as it is based primarily on racial and ethnic bias in US education and employment if algorithms do not include the social data of participants, the computerized systems can generate biases that are more difficult to identify and, therefore, can exacerbate discrimination in decision making. Carefully designed algorithms might remove certain biases in decision-making (Chicago Booth Review, 2019). However, the effectiveness of the algorithm to prevent discrimination and biases depends on the particular cause, it is being used for. For example, if an algorithm is provided with big data, the social category is an integral parameter for more informed decisions particularly when random selection is involved.
References
Chicago Booth Review. (2019). Line of Inquiry: Sendhil Mullainathan on how AI can help counter human bias [YouTube Video]. Retrieved from https://www.youtube.com/watch?v=R51qRZ2la6I&feature=youtu.be
