Artificial Intelligence Nomenclature Identified From Delphi Study on Key Issues Related to Trust and Barriers to Adoption for Autonomous Systems
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.
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@article{arxiv.2210.09086,
title = {Artificial Intelligence Nomenclature Identified From Delphi Study on Key Issues Related to Trust and Barriers to Adoption for Autonomous Systems},
author = {Thomas E. Doyle and Victoria Tucci and Calvin Zhu and Yifei Zhang and Basem Yassa and Sajjad Rashidiani and Md Asif Khan and Reza Samavi and Michael Noseworthy and Steven Yule},
journal= {arXiv preprint arXiv:2210.09086},
year = {2022}
}
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6 pages