Graduation Year
2022
Document Type
Thesis
Degree
M.S.Cp.
Degree Name
MS in Computer Engineering (M.S.C.P.)
Degree Granting Department
Computer Science and Engineering
Major Professor
John Licato, Ph.D.
Committee Member
Shaun Canavan, Ph.D.
Committee Member
Lawrence Hall, Ph.D.
Keywords
Dimensional, Spaces, Generalization, Evaluation, Metrics
Abstract
In this work, we propose that adopting the methods, principles, and guidelines of the field of psychometrics can help the Artificial Intelligence (AI) community to build more task-generalizable and explainable AI. Three arguments are presented and explored. These arguments are that psychometrics can help by providing 1) a framework for formulating better datasets, 2) psychometric AI data that can lead to models of generalization in AI, and 3) explainable AI through more informative evaluations.
A review of psychometrics and psychological generalization is performed, along with an overview of evaluation, generalization, and explainability in AI. Various ideas are presented throughout for how psychometrics can lead to more task-generalizable and explainable AI. Additionally, in cases where there exists literature exemplifying the points, these works are presented and discussed.
Furthermore, counterarguments to the thesis relevant to each argument, are also presented and discussed. Finally, we conclude the work with a summary and a brief discussion of future directions for research.
Scholar Commons Citation
Braynen, Alec, "Towards More Task-Generalized and Explainable AI Through Psychometrics" (2022). USF Tampa Graduate Theses and Dissertations.
https://digitalcommons.usf.edu/etd/9750
