Lessons from Archives: Strategies for Collecting Sociocultural Data in Machine Learning

TL;DR

This paper emphasizes the importance of systematic data collection in machine learning, especially for sociocultural data, by drawing lessons from archival practices to improve fairness, transparency, and ethics.

Contribution

It advocates for establishing a dedicated ML specialization focused on data collection methodologies inspired by archival and library practices.

Findings

Highlights parallels between archival practices and sociocultural data collection

Identifies key challenges: consent, power, inclusivity, transparency, ethics & privacy

Proposes interdisciplinary approaches to improve data collection in ML

Abstract

A growing body of work shows that many problems in fairness, accountability, transparency, and ethics in machine learning systems are rooted in decisions surrounding the data collection and annotation process. In spite of its fundamental nature however, data collection remains an overlooked part of the machine learning (ML) pipeline. In this paper, we argue that a new specialization should be formed within ML that is focused on methodologies for data collection and annotation: efforts that require institutional frameworks and procedures. Specifically for sociocultural data, parallels can be drawn from archives and libraries. Archives are the longest standing communal effort to gather human information and archive scholars have already developed the language and procedures to address and discuss many challenges pertaining to data collection such as consent, power, inclusivity, transparency, and ethics & privacy. We discuss these five key approaches in document collection practices in archives that can inform data collection in sociocultural ML. By showing data collection practices from another field, we encourage ML research to be more cognizant and systematic in data collection and draw from interdisciplinary expertise.