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Towards More Task-Generalized and Explainable AI through Psychometrics.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Towards More Task-Generalized and Explainable AI through Psychometrics./
Author:	Braynen, Alec.
Description:	1 online resource (51 pages)
Notes:	Source: Masters Abstracts International, Volume: 84-06.
Contained By:	Masters Abstracts International84-06.
Subject:	Computer engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29993741click for full text (PQDT)
ISBN:	9798358463769

Towards More Task-Generalized and Explainable AI through Psychometrics.
Braynen, Alec.

Towards More Task-Generalized and Explainable AI through Psychometrics. - 1 online resource (51 pages)

Source: Masters Abstracts International, Volume: 84-06.

Thesis (M.S.Cp.)--University of South Florida, 2022.

Includes bibliographical references

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.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798358463769Subjects--Topical Terms:

621879
Computer engineering.
Subjects--Index Terms:

Dimensional spacesIndex Terms--Genre/Form:

542853
Electronic books.

Towards More Task-Generalized and Explainable AI through Psychometrics.
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500 $a Source: Masters Abstracts International, Volume: 84-06.
500 $a Advisor: Licato, John.
502 $a Thesis (M.S.Cp.)--University of South Florida, 2022.
504 $a Includes bibliographical references
520 $a 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.
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538 $a Mode of access: World Wide Web
650 4 $a Computer engineering. $3 621879
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653 $a Dimensional spaces
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653 $a Psychometrics
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