Semester of Graduation

Spring 2026

Degree

Master of Science (MS)

Department

Computer Science and Engineering

Document Type

Thesis

Abstract

Artificial intelligence is spreading quickly in workplaces and everyday life, and that fast growth is creating cybersecurity and privacy risks that current laws and organizational practices do not fully address. This thesis asks whether AI-specific knowledge shapes public support for cybersecurity governance of AI-enabled systems in the United States. Using nationally representative survey data from the Pew Research Center’s American Trends Panel (ATP) Wave 119, fielded December 12--18, 2022, I estimate weighted regression models to test how objective AI knowledge shapes support for cybersecurity governance. I also test whether concern about data misuse helps explain that relationship and whether AI knowledge narrows ideological differences in governance attitudes. The results show that higher objective AI knowledge predicts lower support for employer AI job evaluation after controlling for ideology, education, age, and gender. Higher AI knowledge also predicts greater concern that worker data collected through AI would be misused. When misuse concern is added to the model, the AI knowledge effect gets much smaller, which is consistent with a partial explanatory pathway. Predicted-value analysis also shows that ideological differences are largest at low levels of AI knowledge and narrow as AI knowledge increases. Overall, the findings suggest that AI literacy shapes governance attitudes by increasing sensitivity to misuse risk and by reducing ideological gaps. These results highlight the value of accessible public education, transparency, and cybersecurity-focused safeguards for building stronger and more broadly supported AI governance frameworks.

Date

4-15-2026

Committee Chair

Richard, Golden G. Mahmoud, Anas. Ghawaly, James

LSU Acknowledgement

1

LSU Accessibility Acknowledgment

1

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