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

TL;DR

This paper uses a Delphi study with international experts to identify and rank key issues, definitions, and barriers related to trust and adoption of autonomous systems in artificial intelligence.

Contribution

It provides a consensus-based nomenclature and highlights critical trust issues and barriers across multiple disciplines for autonomous AI systems.

Findings

Top concerns and barriers identified and ranked by experts

Summary of literature definitions for AI nomenclature

Cross-disciplinary consensus on trust issues

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.