Abstract:

Artificial intelligence is emerging as an incredibly significant factor that is impacting all aspects of American life, two of the more particularly essential elements of American society that it's impacting is healthcare and education. As an example, AI applications are rapidly facilitating personalized treatment plans with the development of Precision Medicine. The latter observation includes accelerated precision diagnostics with the advent of more exacting 3D image recognition of the human body and associated abnormalities. Such developments can yield markedly improved patient outcomes. Such outcomes are possible because of AI facilitated analysis of vast numbers of datasets with ultra-high speed turnaround time to identify diseased patterns, other abnormalities at the nano level, potential health risks, etc. However, the impact of AI facilitated advances has not uniformly impacted all communities in improved healthcare or education. Underserved and underrepresented American communities are not experiencing improved healthcare or education. Specifically, AI facilitated improvements among underserved and underrepresented African American families and youth are the least likely to experience any improvements in healthcare or education in the United States. The notable exclusion is a perpetration of pervasive historical and contemporary exclusion and a lack of awareness and education of such movements in these communities. Moreover, historical disparities in access to quality education along with limited representation in technology sectors continue to persist. This persistence is a core function of systemic bias in AI algorithms and databases that are used for modeling purposes. Consequently, marginalized underserved and underrepresented African American families and youth collectively are confronted with barriers that severely limit engagement in and benefiting from AI advancements in healthcare and education. This exclusion not only presents barriers to opportunities for educational empowerment but also are reinforcers of the digital divide.

The primary research questions for the current study are:
1) Is there statistically significant differences within and between levels of AI knowledge and AI access experiences among samples of low income, middle income, and high income African American families and youth?
2) Is there statistically significant differences within and between levels of AI knowledge, AI access experiences, and educational attainment among samples of low income, middle income, and high income African American families and youth?
3) Is there statistically significant differences within and between levels of AI knowledge, AI access experiences, educational attainment, and digital access among samples of low income, middle income, and high income African American families and youth?

Systematic secondary samples of data from African American households (N = 734) were generated from the American Trends Panel published by the Pew Research Center. Additionally, geographical sub-samples were disaggregated for the Northeast, Far West, Midwest, and the Southern regions of the U.S. Samples of data were analyzed using the forward stepwise Multi-Nominal Logistic Regression Analysis statistical method. Major findings indicated that African American families and youth had similar levels of AI knowledge and AI access experiences despite differences in socioeconomic and educational attainment.

Keywords:

Artificial intelligence knowledge, AI excess experiences, educational attainment level, algorithms, technology, digital access, socioeconomic status.