Insiders and Outsiders in Research on Machine Learning and Society

TL;DR

This paper examines the social dynamics of machine learning research related to societal issues, revealing that underrepresented researchers are more engaged in this area and exploring the implications of insider/outsider perspectives.

Contribution

It provides a sociological analysis of authorship patterns in ML research on societal issues, highlighting the role of underrepresented groups and the epistemic debates involved.

Findings

Underrepresented researchers are more likely to work on societal issues in ML.

Authorship patterns reveal social boundaries and insider/outsider dynamics.

Epistemic debates about lived experience influence research boundaries.

Abstract

A subset of machine learning research intersects with societal issues, including fairness, accountability and transparency, as well as the use of machine learning for social good. In this work, we analyze the scholars contributing to this research at the intersection of machine learning and society through the lens of the sociology of science. By analyzing the authorship of all machine learning papers posted to arXiv, we show that compared to researchers from overrepresented backgrounds (defined by gender and race/ethnicity), researchers from underrepresented backgrounds are more likely to conduct research at this intersection than other kinds of machine learning research. This state of affairs leads to contention between two perspectives on insiders and outsiders in the scientific enterprise: outsiders being those outside the group being studied, and outsiders being those who have not participated as researchers in an area historically. This contention manifests as an epistemic question on the validity of knowledge derived from lived experience in machine learning research, and predicts boundary work that we see in a real-world example.