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Artificial Intelligence Nomenclature Identified From Delphi Study on Key Issues Related to Trust and Barriers to Adoption for Autonomous Systems

Year: 2022

Type: preprint

Abstract: The rapid integration of artificial intelligence across traditional research domains has generated an amalgamation of nomenclature. As cross-discipline teams work together on complex machine learning challenges, finding a consensus of basic definitions in the literature is a more fundamental problem. As a step in the Delphi process to define issues with trust and barriers to the adoption of autonomous systems, our study first collected and ranked the top concerns from a panel of international experts from the fields of engineering, computer science, medicine, aerospace, and defence, with experience working with artificial intelligence. This document presents a summary of the literature definitions for nomenclature derived from expert feedback.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2210.09086

Authors: Doyle, Thomas E., Tucci, Victoria, Zhu, Calvin, Zhang, Yifei, Yassa, Basem, Rashidiani, Sajjad, Khan, Md Asif, Samavi, Reza, Noseworthy, Michael, Yule, Steven

Institutions: Vector Institute, McMaster University, Toronto Metropolitan University

Language: English

Topic: Artificial Intelligence Applications

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: McMaster University, Ministère de la Défense Nationale

OpenAlex ID: W4306808369

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