Graduation Year


Document Type




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.


Dimensional, Spaces, Generalization, Evaluation, Metrics


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.