Date of Award
2025
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Computer Science
Committee Chair
Vineetha Menon
Committee Member
Kristin Weger
Committee Member
Jacob Hauenstein
Research Advisor
Vineetha Menon
Subject(s)
Artificial intelligence, Trust, Human-computer interaction, Explainable AI (XAI)
Abstract
Explainable Artificial Intelligence aims to open up the “black box” of artificial intelligence, thereby improving user trust, decreasing cognitive load, and improving task performance. As the number of machine learning models has grown, so has the number of explanation methods, making it necessary to select the most suitable explanation method for a given context. However, current selection methods often fail to account for all the factors that contribute significantly to an explanation method’s suitability. This thesis proposes a novel methodology for selecting and evaluating explanation methods. Subjective workload, objective performance, and feasibility are considered. This three-pronged “pitchfork” methodology is put to use by evaluating the performance of LIME and Kernel SHAP in a simulated environment where users are tasked with rescuing people. Quantitative data relating to subjective workload, objective performance, and feasibility is captured and analyzed in order to determine the most suitable explanation for the context.
Recommended Citation
Taylor, Josiah, "A comprehensive study on explainable artificial intelligence : a user-centric, cognitive workload, and feasibility-driven analysis" (2025). Theses. 803.
https://louis.uah.edu/uah-theses/803