Doctoral Dissertations

Keywords and Phrases

Explainable AI; Human AI Interaction; Human Computer Interaction; Human Subject Studies; Qualitative Research; Quantitative Research

Abstract

Explainable AI (XAI) aims to unravel the "black box" nature of AI systems and provide insights into the inner workings that lead to a prediction. However, the best XAI communication varies depending on the individual, task, and broader context. It is challenging to anticipate the best XAI for a particular use case. One strategy is for users to build an appropriate mental model of AI with both prediction and system level XAI. To date, little research has focused on quantitatively measuring users interacting with system-level XAI. This dissertation has three primary contributions. The first contribution is a scoping review paper focused on developing AI for kidney transplant placement identifying the need for user-driven customization capabilities. This work highlights the need for stakeholders’ input when designing an AI system and how much information they desire to review for each case, depending on user expertise, task complexity, and AI literacy. The second contribution focuses on the impact of prediction- level information by measuring user task performance and confidence when they receive multiple AI recommendations along with AI’s uncertainty information. The third contribution is a series of experiments to measure discernment (i.e., people’s ability to evaluate the capability of an AI) to understand a user’s mental model of AI. These studies support efforts to build theory and develop an interface for an AI system that can be integrated into the kidney transplant placement process to reduce the kidney non- utilization rate.

Advisor(s)

Canfield, Casey I.

Committee Member(s)

Dagli, Cihan H., 1949-
Shank, Daniel Burton
Marley, Robert J.
Allada, Venkat

Department(s)

Engineering Management and Systems Engineering

Degree Name

Ph. D. in Engineering Management

Publisher

Missouri University of Science and Technology

Publication Date

Summer 2025

Journal article titles appearing in thesis/dissertation

Paper I, found on pages 8 – 53, has been submitted to Artificial Intelligence in Medicine.

Paper II, found on pages 54 – 82, has been published in the Journal of Risk Research.

Paper III, found on pages 83 – 161, is intended for submission to Computers in Human Behavior.

Pagination

xv, 200 pages

Note about bibliography

Includes_bibliographical_references_(pages 195-198)

Rights

© 2025 Harishankar Vasudevanallur Subramanian , All Rights Reserved

Document Type

Dissertation - Open Access

File Type

text

Language

English

Thesis Number

T 12551

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Included in

Engineering Commons

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