Affirmative action came in as a solution to unlawful discrimination, prior discrimination, and as a preventive mechanism against future occurrences. Minorities in the United States have historically experienced the worst outcomes in terms of this issue, and it is a demographic women of color belong to. Affirmative action alleviated the plight of this group, and rescinding it will have significant ramifications. The social lens approach emerges as the optimal way to get all stakeholders to see the importance of enforcing these procedures and the effects of doing away with it. The process of achieving that outcome is influenced by biases and assumptions and the obstacle of limited knowledge.
The most significant factors that could affect public education on the importance of affirmative action to women of color and the effects of rescinding it are biases and assumptions. Biases will be especially prevalent in populations that experience the best outcomes under a system without these anti-discrimination procedures (Saxena et al., 2019). These individuals will have a distorted perspective because they would prefer to function in conditions that favor them. Assumptions will come from well-meaning Americans who think people from all backgrounds operate on a level playing field (Woodruff et al., 2018). This factor will manifest in the form of calls to work harder and stop complaining.
One potential obstacle that will stand in the way of efficient public education is limited knowledge. Many Americans are unaware of the plight of women of color and how important affirmative action is to their outcomes, and the ramifications of rescinding the same (Espín, 2019). The impact will manifest in little interest in the topic. A possible outcome of the proposed engagement that will influence the social conversations that add to critical awareness of diversity will be increased public awareness. More people will be informed about the situation this demographic finds itself in, which will facilitate higher-quality correspondence between stakeholders.
References
Espín, O. M. (2019). Latina Realities. Routledge. Web.
Saxena, N. A., Huang, K., DeFilippis, E., Radanovic, G., Parkes, D. C., & Liu, Y. (2019). How do fairness definitions fare? Examining public attitudes towards algorithmic definitions of fairness. In Proceedings of the 2019 AAAI/ACM conference on AI, ethics, and society (AIES ‘19) (pp. 99-106). Association for Computing Machinery. Web.
Woodruff, A., Fox, S. E., Rousso-Schindler, S., & Warshaw, J. (2018). A qualitative exploration of perceptions of algorithmic fairness. In Proceedings of the 2018 chi conference on human factors in computing systems (CHI ’18) (pp. 1-14). Association for Computing Machinery. Web.