Artificial Intelligence Nomenclature Identified From Delphi Study on Key Issues Related to Trust and Barriers to Adoption for Autonomous Systems
This addresses the fundamental problem of terminology confusion for cross-discipline teams working on AI and autonomous systems, but it is incremental as it focuses on defining issues rather than solving them.
The study tackled the problem of inconsistent AI nomenclature across disciplines by collecting and ranking top concerns from international experts to define key issues related to trust and barriers to adoption for autonomous systems, resulting in a summary of literature definitions derived from expert feedback.
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.