What Makes An Expert? Reviewing How ML Researchers Define "Expert"

TL;DR

This paper reviews 112 studies to understand how ML researchers define and utilize 'expertise', revealing that expertise is often vaguely defined and mainly involves textbook knowledge, impacting responsible AI development.

Contribution

It provides a comprehensive review of how expertise is characterized and engaged in ML research, highlighting gaps and implications for responsible AI practices.

Findings

Expertise is often undefined in ML research.

Knowledge outside formal education is rarely considered.

Experts mainly contribute through data annotation.

Abstract

Human experts are often engaged in the development of machine learning systems to collect and validate data, consult on algorithm development, and evaluate system performance. At the same time, who counts as an 'expert' and what constitutes 'expertise' is not always explicitly defined. In this work, we review 112 academic publications that explicitly reference 'expert' and 'expertise' and that describe the development of machine learning (ML) systems to survey how expertise is characterized and the role experts play. We find that expertise is often undefined and forms of knowledge outside of formal education and professional certification are rarely sought, which has implications for the kinds of knowledge that are recognized and legitimized in ML development. Moreover, we find that expert knowledge tends to be utilized in ways focused on mining textbook knowledge, such as through data annotation. We discuss the ways experts are engaged in ML development in relation to deskilling, the social construction of expertise, and implications for responsible AI development. We point to a need for reflection and specificity in justifications of domain expert engagement, both as a matter of documentation and reproducibility, as well as a matter of broadening the range of recognized expertise.