Date of Award
3-2024
Document Type
Thesis
Degree Name
Master of Science in Systems Engineering
Department
Department of Systems Engineering and Management
First Advisor
John J. Elshaw, PhD
Abstract
This thesis investigates the impact of adjusting artificial intelligence explainability levels’ outputs on user perception. The overarching study extends within the Explainable Artificial Intelligence (XAI) domain. It examines a spectrum of variables, including performance, cognizance, familiarity, transparency, system bias, and the overall impact of AI, to understand their collective and individual effects that enable effective professional use in an organization. The study aims to illuminate the relationship between the degree of explainability provided by large language models such as ChatGPT, Bard, and Bing AI and the performance of these models when tasked with XAI adjustments.
AFIT Designator
AFIT-ENV-MS-24-M-148
Recommended Citation
Neil, Anthony J., "Artificial Intelligence and Perception: An Empirical Study" (2024). Theses and Dissertations. 7763.
https://scholar.afit.edu/etd/7763
Included in
Artificial Intelligence and Robotics Commons, Communication Commons, Systems Engineering Commons
Comments
A 12-month embargo was observed for posting this work on AFIT Scholar.
Distribution Statement A, Approved for Public Release. PA case number on file.